mamba心脏疾病诊断

1. medmamba复现

想首先尝试一下medmamba的实验复现，用做后续的基础架构

网络结构图

实验结果

是二维的，不好改，还是改改之前的分割网络看看

2. echo-mi实验

2.1 idea

使用双切面（二腔室+四腔室）超声心动图数据训练Mamba模型诊断早期心梗的需求，设计了一个创新性的双路径时空融合Mamba架构（Dual-Path Spatiotemporal Fusion Mamba, DPSF-Mamba）。该架构针对性解决多切面心脏超声的时空特征融合问题，核心思路如下：

一、核心改造思路

双路径异构特征提取

独立编码路径：为二腔室（2C）和四腔室（4C）切面分别设计专用Mamba块，适应不同视角的局部结构特征。
2C路径：聚焦左心室前壁、心尖部运动异常（早期心梗敏感区域）。

4C路径：捕获室间隔、侧壁运动及整体心室协调性（诊断关键指标)。

动态门控融合模块（DGFM）：引入可学习的门控权重，自适应融合双路径特征（公式示例）：

F_{fused} = \sigma(W_g \cdot [F_{2C}, F_{4C}]) \odot F_{2C} + (1-\sigma(W_g \cdot [F_{2C}, F_{4C}])) \odot F_{4C}

其中 $W_g$ 为可训练权重， $\sigma$ 为Sigmoid函数，实现特征重要性动态分配。

多任务联合优化分类头

//todo

二、创新技术优势

模块	传统Mamba局限	DPSF-Mamba改进	临床价值
多切面处理	单一路径忽视视角差异	双路径异构编码 + 动态融合	减少视角偏差，提升小病灶敏感性
时空特征利用	长序列建模但空间关联弱	时空切片重组 + 坐标注意力	同步捕捉运动异常与结构变形
数据效率	需大量标注数据	多任务学习共享特征	缓解超声标注稀缺问题
可解释性	黑盒决策	门控权重可视化切面贡献度	辅助医生理解AI诊断依据

三、预期效果验证方案

可解释性分析

绘制门控权重热力图（如下示例），验证模型对病变切面的关注度：

前壁梗死患者：4C切面权重峰值达0.83（主导诊断）
心尖梗死患者：2C切面权重升至0.79

四、潜在挑战与解决方案

挑战1：双切面数据不全（如部分患者缺失一个切面）
方案：引入跨切面知识蒸馏，用完整数据训练教师网络指导单切面学生网络。
挑战2：超声伪影干扰
方案：在Mamba前端加入对抗去噪模块（如Conditional GAN）。

2.2 faec_advance实验

name	acc	auc	f1	precision	recall	specificity
faec	0.71875	0.7013	0.3077	1.0	0.1818	1.0

camus 多切面

name	acc	auc	f1	precision	recall	specificity
Experiment_6 fold_1	0.8125	0.858333	0.769230	0.714286	0.833333	0.8
Experiment_6 fold_2	0.75	0.741666	0.555555	0.833333	0.416666	0.949999
Experiment_6 fold_3	0.8125	0.899999	0.699999	0.875	0.583333	0.949999
Experiment_6 fold_4	0.78125	0.852814	0.588235	0.833333	0.454545	0.952380
Experiment_6 fold_5	0.71875	0.701298	0.307692	1.0	0.1818	1.0
Experiment_6 avg	0.775	0.8108	0.5841	0.8512	0.4939	0.9305

3. MIMamba实验

改造之前的分割网络的mamba用于分类

3.1 camus数据集

camus A2C的实验

name	acc	auc	f1	precision	recall
Experiment_4 fold_1	0.375	0.533333	0.545454	0.375	1.0
Experiment_4 fold_2	0.34375	0.558441	0.511628	0.34375	1.0
Experiment_5 fold_1	0.75	0.699999	0.636364	0.699999	0.583333
Experiment_5 fold_2	0.71875	0.670995	0.571428	0.6	0.545454
Experiment_5 fold_3	0.71875	0.636363	0.470588	0.571428	0.4
Experiment_5 fold_4	0.71875	0.590909	0.470588	0.571428	0.4
Experiment_5 fold_5	0.625	0.490909	0.4	0.4	0.4
Experiment_5 avg	0.70625	0.617835	0.509794	0.568571	0.425757

camus 多切面

name	acc	auc	f1	precision	recall	specificity
Experiment_7 fold_1	0.84375	0.837499	0.761904	0.888888	0.666666	0.949999
Experiment_7 fold_2	0.75	0.708333	0.555555	0.833333	0.416666	0.949999
Experiment_7 fold_3	0.8125	0.8375	0.727272	0.8	0.666666	0.899999
Experiment_7 fold_4	0.8125	0.818181	0.75	0.692307	0.818181	0.809523
Experiment_7 fold_5	0.71875	0.714285	0.307692	1.0	0.181818	1.0
Experiment_7 (监控acc)	0.7875	0.7832	0.6205	0.8429	0.5500	0.9219
Experiment_8 fold_1	0.65625	0.875	0.266666	0.666666	0.166666	0.949999
Experiment_8 fold_2	0.75	0.816666	0.666666	0.666666	0.666666	0.8
Experiment_8 fold_3	0.71875	0.858333	0.571428	0.666666	0.5	0.85
Experiment_8 fold_4	0.65625	0.8658	0.0	0	0	1
Experiment_8 fold_5	0.6875	0.796536	0.583333	0.538461	0.636363	0.714285
Experiment_8 (监控auc)
Experiment_9 fold_1	0.75	0.879166	0.5	1.0	0.333333	1.0
Experiment_9 fold_2	0.65625	0.695833	0.153846	1.0	0.083333	1.0
Experiment_9 fold_3	0.65625	0.770833	0.153846	1.0	0.083333	1.0
Experiment_9 fold_4	0.6875	0.649350	0.166666	1.0	0.090909	1.0
Experiment_9 fold_5	0.75	0.761904	0.5	0.8	0.363636	0.952380
Experiment_9 (precision)
Experiment_10 fold_1	0.84375	0.875	0.814814	0.733333	0.916666	0.8
Experiment_10 fold_2	0.78125	0.75	0.695652	0.727272	0.666666	0.85
Experiment_10 fold_3	0.84375	0.85	0.761904	0.888888	0.666666	0.949999
Experiment_10 fold_4	0.75	0.779220	0.692307	0.6	0.818181	0.714285
Experiment_10 fold_5	0.625	0.675324	0.625	0.476190	0.909090	0.476190
Experiment_10 (监控f1)	0.7688	0.7859	0.7179	0.6851	0.7955	0.7581

Experiment_11 fold_1	0.90625	0.85	0.869565	0.909090	0.833333	0.949999
Experiment_11 fold_2	0.75	0.754166	0.714285	0.625	0.833333	0.699999
Experiment_11 fold_3	0.78125	0.820833	0.740740	0.666666	0.833333	0.75
Experiment_11 fold_4	0.75	0.744588	0.714285	0.588235	0.909090	0.666666
Experiment_11 fold_5	0.65625	0.679653	0.645161	0.5	0.909090	0.523809

Experiment_best	0.8063	0.8129	0.7321	0.7512	0.7242	0.8547

Experiment_15 fold_1	0.875	0.870833	0.846153	0.785714	0.916666	0.85
Experiment_15 fold_2	0.8125	0.762499	0.769230	0.714285	0.833333	0.8
Experiment_15 fold_3	0.84375	0.854166	0.761904	0.888888	0.666666	0.949999
Experiment_15 fold_4	0.75	0.709956	0.6	0.666666	0.545454	0.857142
Experiment_15 fold_5	0.75	0.718614	0.692307	0.6	0.818181	0.714285
Experiment_15
Experiment_18 fold_1	0.78125	0.725	0.72	0.6923	0.75	0.8
Experiment_23 fold_1	0.78125	0.7625	0.6666	0.7777	0.5833	0.8999
Experiment_24 fold_1	0.8125	0.8166	0.6999	0.875	0.5833	0.949999
Experiment_25 fold_1	0.84375	0.862499	0.761904	0.888888	0.666666	0.949999

使用adaw

name	acc	auc	f1	precision	recall	specificity
Experiment_28 fold_1	0.8125	0.791666	0.75	0.75	0.75	0.85
Experiment_29 fold_1	0.8125	0.7958	0.75	0.75	0.75	0.85

camus mi_mamba A2C和A4C路径

1	`d_state: 16 d_state: 32`

name	acc	auc	f1	precision	recall	specificity
Experiment_26 fold_1	0.78125	0.766666	0.72	0.692307	0.75	0.8

换用 adaw

name	acc	auc	f1	precision	recall	specificity
Experiment_27 fold_1	0.8125	0.75	0.75	0.75	0.75	0.85

camus 多切面 MultiStageFusion

name	acc	auc	f1	precision	recall	specificity
Experiment_12 fold_1	0.8125	0.866666	0.699999	0.875	0.583333	0.949999
Experiment_12 fold_2	0.75	0.720833	0.666666	0.666666	0.666666	0.8
Experiment_12 fold_3	0.78125	0.75	0.631579	0.857142	0.5	0.949
Experiment_13 fold_1	0.8125	0.754166	0.699999	0.875	0.583333	0.949999
Experiment_14 fold_1	0.8125	0.891666	0.785714	0.6875	0.916666	0.75

camus 多尺度融合模型

name	acc	auc	f1	precision	recall	specificity
Experiment_17 fold_1	0.71875	0.695833	0.666666	0.6	0.75	0.699999

camus 分层融合模型mi_mamba_hierarchical_model

name	acc	auc	f1	precision	recall	specificity
Experiment_16 fold_1	0.78125	0.8208	0.7407	0.666666	0.833333	0.75
Experiment_16 fold_2	0.6875	0.5208	0.375	0.75	0.25	0.9499
Experiment_16 fold_3	0.75	0.8	0.6	0.75	0.5	0.8999
Experiment_16 fold_4	0.75	0.6883	0.6	0.666666	0.545454	0.857142
Experiment_16 fold_5	0.6875	0.580086	0.375	0.6	0.272727	0.904762
Experiment_19 fold_1	0.75	0.758333	0.5555
Experiment_20 fold_1	0.75	0.7333	0.6666	0.666666	0.666666	0.8
Experiment_21 fold_1	0.78125	0.75	0.695652	0.727272	0.666666	0.85
Experiment_22 fold_1	0.75	0.6875	0.5555	0.8333	0.416666	0.9499
	0.75	0.6958	0.6	0.75	0.5	0.899999
换用adaw
Experiment_30 fold_1	0.78125	0.708333	0.666666	0.777777	0.583333	0.899999

cross-ssm

name	acc	auc	f1	precision	recall	specificity
Experiment_32 fold_1	0.625	0.475	0	0	0	1
Experiment_33 fold_1	0.78125	0.783333	0.666666	0.777777	0.583333	0.899999
Experiment_34 fold_1 gate	0.75	0.754166	0.636363	0.699999	0.583333	0.85
Experiment_35 fold_1 gate w	0.8125	0.804166	0.727272	0.8	0.666666	0.899999
Experiment_36 fold_1 attn w	0.75	0.720833	0.6	0.75	0.5	0.899999
Experiment_37 fold_1 gate attn w	0.8125	0.825	0.727272	0.8	0.666666	0.899999
Experiment_ fold_1 attn gate w	0.75	0.774999	0.636363	0.699999	0.583333	0.85

从上面的来看cross-ssm效果还可以，继续使用cross-ssm 验证camus填充方式

cross-ssm gate attn w

name	acc	auc	f1	precision	recall	specificity
Experiment_39 fold_1 repeat	0.78125	0.729166	0.666666	0.777777	0.583333	0.899999
Experiment_40 fold_1 interpolate	0.84375	0.845833	0.8	0.769230	0.833333	0.85
Experiment_40 fold_1 random_repeat	0.75	0.754166	0.666666	0.666666	0.666666	0.8
Experiment_41 fold_1 cyclic	0.8125	0.791666	0.75	0.75	0.75	0.85
Experiment_42 fold_1 reflect	0.71875	0.745833	0.666666	0.6	0.75	0.699999
Experiment_43 fold_1 noise	0.75	0.745833	0.692307	0.642857	0.75	0.75
Experiment_44 fold_1 random	0.8125	0.783333	0.727272	0.8	0.666666	0.899999

hidden size降低到256再试试

name	acc	auc	f1	precision	recall	specificity
Experiment_60 fold_1 repeat	0.75	0.766666	0.636363	0.699999	0.583333	0.85
Experiment_61 fold_1 interpolate	0.78125	0.829166	0.666666	0.777777	0.583333	0.899999
Experiment_62 fold_1 random_repeat	0.8125	0.8083	0.7692	0.714285	0.833333	0.8
Experiment_63 fold_1 cyclic	0.78125	0.716666	0.72	0.6923	0.75	0.8
Experiment_64 fold_1 reflect	0.78125	0.795833	0.666666	0.777777	0.583333	0.899999
Experiment_65 fold_1 noise	0.8125	0.795833	0.75	0.75	0.75	0.85
Experiment_66 fold_1 random	0.78125	0.7875	0.695652	0.727272	0.666666	0.85

cross-ssm

name	acc	auc	f1	precision	recall	specificity
Experiment_45 fold_1	0.84375	0.858333	0.761904	0.888888	0.666666	0.949999
Experiment_45 fold_1	0.78125	0.8	0.72	0.692307	0.75	0.8
Experiment_46 fold_1	0.71875	0.704166	0.666666	0.6	0.75	0.699999
Experiment_47 fold_1	0.78125	0.758333	0.695652	0.727272	0.666666	0.85
Experiment_48 fold_1 b8	0.78125	0.779166	0.72	0.692307	0.75	0.8
Experiment_49 fold_1 b16	0.78125	0.758333	0.740740	0.666666	0.833333	0.75
增加模型feat[32, 64, 128, 256]
Experiment_50 fold_1 b8	0.78125	0.741666	0.740740	0.666666	0.833333	0.75
Experiment_51 fold_1 b8	0.78125	0.7875	0.758620	0.647058	0.916666	0.699999
Experiment_52 fold_1 b8	0.75	0.804166	0.714285	0.625	0.833333	0.699999
改回了填充到32帧
Experiment_53 fold_1	0.78125	0.779166	0.72	0.6923	0.75	0.8
Experiment_54 fold_1	0.78125	0.7625	0.666666	0.7777	0.583333	0.899999
增大hidden size 512
Experiment_56 fold_1	0.71875	0.666666	0.666666	0.6	0.75	0.699999
Experiment_57 fold_1	0.78125	0.729166	0.666666	0.777777	0.583333	0.899999
hidden size 256
Experiment_58 fold_1	0.78125	0.754166	0.740740	0.666666	0.833333	0.75
hidden size 768
Experiment_59 fold_1	0.78125	0.7875	0.6315	0.8571	0.5	0.9499

前面的损失都是bce

损失

name	acc	auc	f1	precision	recall	specificity
Experiment_67 fold_1 focal	0.6875	0.770833	0.545454	0.6	0.5	0.8
Experiment_68 fold_1 dice	0.75	0.779166	0.6	0.75	0.5	0.899999
Experiment_69 fold_1 bce_dice	0.75	0.729166	0.636363	0.699999	0.583333	0.85
Experiment_70 fold_1 tversky	0.875	0.829166	0.846153	0.785714	0.916666	0.85
Experiment_71 fold_1 asymmetric	0.78125	0.766666	0.72	0.6923	0.75	0.8

tversky 完整实验

name	acc	auc	f1	precision	recall	specificity
Experiment_70 fold_1	0.875	0.829166	0.846153	0.785714	0.916666	0.85
Experiment_70 fold_2	0.75	0.675	0.666666	0.666666	0.666666	0.8
Experiment_70 fold_3	0.71875	0.691666	0.526315	0.714285	0.416666	0.899999
Experiment_70 fold_4	0.71875	0.718614	0.608695	0.583333	0.636363	0.761904
Experiment_70 fold_5	0.5625	0.597402	0.533333	0.421052	0.727272	0.476190

还是改为16帧吧

name	acc	auc	f1	precision	recall	specificity
Experiment_74 fold_1	0.84375	0.770833	0.782608	0.818181	0.75	0.899999
Experiment_74 fold_2	0.6875	0.645833	0.375	0.75	0.25	0.949999
Experiment_74 fold_3	0.6875	0.6875	0.5454	0.6	0.5	0.8
Experiment_74 fold_4	0.78125	0.744588	0.666666	0.699999	0.636363	0.857142
Experiment_74 fold_5	0.6875	0.670995	0.583333	0.538461	0.636363	0.714285

bce损失

name	acc	auc	f1	precision	recall	specificity
Experiment_75 fold_1	0.8125	0.7666	0.75	0.75	0.75	0.85
Experiment_75 fold_2	0.6875	0.637499	0.444444	0.666666	0.333333	0.899999
Experiment_75 fold_3	0.6875	0.612499	0.583333	0.583333	0.583333	0.75
Experiment_75 fold_4	0.75	0.766233	0.555555	0.714285	0.454545	0.904761
Experiment_75 fold_5	0.59375	0.683982	0.580645	0.449999	0.818181	0.476190

去掉模块

bce

name	acc	auc	f1	precision	recall	specificity
Experiment_76 fold_1	0.8125	0.8292	0.7273	0.8	0.6667	0.8999
Experiment_76 fold_2	0.75	0.7083	0.6923	0.6429	0.75	0.75
Experiment_76 fold_3	0.75	0.7916	0.6364	0.6999	0.5833	0.85
Experiment_76 fold_4	0.78125	0.7359	0.6666	0.6999	0.6363	0.8571
Experiment_76 fold_5	0.4375	0.5757	0.5263	0.3703	0.9090	0.1904

只加上gated

name	acc	auc	f1	precision	recall	specificity
Experiment_77 fold_1	0.6875	0.7375	0.6666	0.5555	0.8333	0.6
Experiment_77 fold_2	0.7187	0.6666	0.64	0.6153	0.6666	0.75
Experiment_77 fold_3	0.5937	0.5916	0.6285	0.4782	0.9166	0.4
Experiment_77 fold_4	0.5625	0.6147	0.5882	0.4347	0.9090	0.3809
Experiment_77 fold_5

只加上attn

name	acc	auc	f1	precision	recall	specificity
Experiment_78 fold_1	0.75	0.7541	0.6666	0.6666	0.6666	0.8
Experiment_78 fold_2	0.71875	0.6916	0.64	0.6153	0.6666	0.75
Experiment_78 fold_3	0.625	0.6208	0.6	0.5	0.75	0.55
Experiment_78 fold_4	0.7187	0.5930	0.5263	0.625	0.4545	0.8571
Experiment_78 fold_5

3.1.1 mamba+echoprime video

使用两个分支对两个切面的数据进行提取，echo_prime和mamba进行提取，最后一起送入分类器

mamba: A2C切面的数据

echo_prime: A4C切面的数据

融合: 通道拼接

name	acc	auc	f1	precision	recall	specificity
Experiment_79 fold_1	0.7813	0.7333	0.6667	0.7777	0.5833	0.90
Experiment_79 fold_2	0.78125	0.774999	0.5882	1.0	0.4166	1.0
Experiment_79 fold_3	0.875	0.9208	0.818181	0.899999	0.75	0.949999
Experiment_79 fold_4	0.84375	0.839826	0.761904	0.8	0.727272	0.904761
Experiment_79 fold_5	0.78125	0.757575	0.6666	0.699999	0.636363	0.857142
Experiment_79	0.8125

保留2*2*2的空间结构

name	acc	auc	f1	precision	recall	specificity
Experiment_80 fold_1	0.78125	0.695833	0.666666	0.777777	0.5833	0.8999
Experiment_80 fold_2	0.78125	0.787499	0.695652	0.727272	0.666666	0.85
Experiment_80 fold_3	0.875	0.8958	0.846153	0.785714	0.916666	0.85
Experiment_80 fold_4	0.84375	0.839826	0.782608	0.75	0.818181	0.857142
Experiment_80 fold_5	0.8125	0.787878	0.6666	0.8571	0.5454	0.9523
Experiment_80	0.8188	0.8014	0.7315	0.7796	0.7060	0.8818

保留4*4*4的空间结构

name	acc	auc	f1	precision	recall	specificity
Experiment_81 fold_1	0.8125	0.875	0.6999	0.875	0.5833	0.9499
Experiment_81 fold_2	0.8125	0.775	0.7272	0.8	0.666666	0.899999
Experiment_81 fold_3	0.875	0.879166	0.8333	0.8333	0.8333	0.8999
Experiment_81 fold_4	0.84375	0.848484	0.7826	0.75	0.818181	0.857142
Experiment_81 fold_5	0.84375	0.7922	0.7619	0.8	0.727272	0.904761
Experiment_80	0.8375	0.8139	0.7610	0.8117	0.7257	0.9023

交换特征提取器

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构

name	acc	auc	f1	precision	recall	specificity
Experiment_82 fold_1	0.90625	0.879166	0.879999	0.846153	0.916666	0.899999
Experiment_82 fold_2	0.78125	0.741666	0.695652	0.727272	0.666666	0.85
Experiment_82 fold_3	0.8125	0.8666	0.7692	0.714285	0.8333	0.8
Experiment_82 fold_4	0.90625	0.9220	0.842105	1.0	0.727272	1.0
Experiment_82 fold_5	0.75	0.6666	0.5	0.8	0.363636	0.9523
Experiment_82	0.8312	0.8152	0.7373	0.8175	0.6976	0.8805

学习率

name	acc	auc	f1	precision	recall	specificity
Experiment_83 fold_5 1e-4	0.71875	0.6320	0.3077	1.0	0.1818	1.0
Experiment_84 fold_5 1e-6	0.78125	0.7445	0.5882	0.8333	0.454545	0.952380

name	acc	auc	f1	precision	recall	specificity
Experiment_84 fold_1	0.9375	0.904166	0.916666	0.916666	0.916666	0.949999
Experiment_84 fold_2	0.78125	0.699999	0.72	0.692307	0.75	0.8
Experiment_84 fold_3	0.84375	0.816666	0.8	0.769230	0.833333	0.85
Experiment_84 fold_4	0.90625	0.8961	0.8571	0.8999	0.818181	0.952380
Experiment_84 fold_5	0.78125	0.7445	0.5882	0.8333	0.454545	0.952380
Experiment_84 95%	0.8500	0.8249	0.7857	0.8148	0.7586	0.9020

3.1.2 mamba+echoprime text video

在mamba的decoder部分加入三层知识向量

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构损失bce

name	acc	auc	f1	precision	recall	specificity
Experiment_102 fold_1	0.9375	0.895833	0.916666	0.916666	0.916666	0.949999
Experiment_102 fold_2	0.78125	0.7374	0.6666	0.7777	0.5833	0.8999
Experiment_102 fold_3	0.78125
Experiment_102 fold_4	0.875
Experiment_102 fold_5	0.75
Experiment_102	0.816

在mamba的decoder部分加入知识向量和echo prime视频提取部分都加入知识向量

学习率1e-5

name	acc	auc	f1	precision	recall	specificity
Experiment_103 fold_1	0.90625	0.8791	0.8799	0.8461	0.9166	0.8999
Experiment_103 fold_2	0.78125
Experiment_103 fold_3	0.8125
Experiment_103 fold_4	0.875	0.8744	0.8	0.8888	0.7272	0.9523
Experiment_103 fold_5	0.75
Experiment_103	0.825

损失asymmetric 学习率1e-5

name	acc	auc	f1	precision	recall	specificity
Experiment_104 fold_1	0.90625
Experiment_104 fold_2	0.78125
Experiment_104 fold_5	0.75

学习率

name	acc	auc	f1	precision	recall	specificity
Experiment_105 fold_5 1e-6	0.71875
Experiment_106 fold_5 1e-5	0.75
Experiment_107 fold_5 1e-4	0.71875
Experiment_108 fold_5 3e-4	0.75
Experiment_109 fold_5 3e-5	0.6875
Experiment_110 fold_5 3e-6	0.71875

效果好差，还是只加一层知识向量吧

name	acc	auc	f1	precision	recall	specificity
Experiment_111 fold_5 1e-6	0.75

在A4C的mamba的瓶颈层加入知识向量，同时A2C的video encoder也加入知识向量

name	acc	auc	f1	precision	recall	specificity
Experiment_112 fold_1	0.9375	0.925	0.916666	0.916666	0.916666	0.9499
Experiment_112 fold_2	0.78125	0.7374	0.6666	0.7777	0.5833	0.8999
Experiment_112 fold_3	0.78125	0.7958	0.72	0.6923	0.75	0.8
Experiment_112 fold_4	0.875	0.8701	0.8181	0.818181	0.818181	0.9047
Experiment_112 fold_5
Experiment_112

b16

name	acc	auc	f1	precision	recall	specificity
Experiment_113 fold_1	0.90625	0.9208	0.8799	0.8461	0.9166	0.8999
Experiment_113 fold_2	0.78125	0.725	0.72	0.6923	0.75	0.8
Experiment_113 fold_3	0.75	0.8125	0.7142	0.625	0.8333	0.6999
Experiment_113 fold_4	0.90625	0.8658	0.8571	0.8999	0.8181	0.9523
Experiment_113 fold_5	0.78125	0.6666	0.6666	0.6999	0.6363	0.8571
Experiment_113

交换A2C和A4C 十分拉跨

name	acc	auc	f1	precision	recall	specificity
Experiment_114 fold_1	0.75	0.8458	0.7333	0.6111	0.9166	0.6499
Experiment_114 fold_2	0.8125	0.875	0.7692	0.7142	0.8333	0.8
Experiment_114 fold_3	0.78125	0.825	0.7586	0.6470	0.9166	0.6999
Experiment_114 fold_4	0.6875	0.7748	0.6428	0.5294	0.8181	0.6190
Experiment_114 fold_5	0.75	0.6926	0.6666	0.6153	0.7272	0.7619

在mamba的decoder部分加入知识向量和echo prime video encoder 加入知识向量

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构损失bce

学习率1e-6 b8

name	acc	auc	f1	precision	recall	specificity
Experiment_115 fold_1	0.9375	0.9125	0.9166	0.9166	0.9166	0.9499
Experiment_115 fold_2	0.8125	0.7333	0.75	0.75	0.75	0.85
Experiment_115 fold_3	0.8125	0.8041	0.7272	0.8	0.6666	0.8999
Experiment_115 fold_4	0.90625	0.9004	0.8571	0.8999	0.8181	0.9523
Experiment_115 fold_5	0.78125	0.7835	0.5882	0.8333	0.4545	0.9523
Experiment_115 95%	0.8500	0.8315	0.7778	0.8400	0.7241	0.9216

3.1.3 orginmutimodel

name	acc	auc	f1	precision	recall	specificity
Experiment_132 fold_1	0.78125	0.7041	0.6315	0.8571	0.5	0.9499
Experiment_132 fold_2	0.78125	0.7083	0.6666	0.7777	0.5833	0.8999
Experiment_132 fold_3	0.71875	0.6666	0.64	0.6153	0.6666	0.75
Experiment_132 fold_4	0.8125	0.8095	0.75	0.6923	0.8181	0.8095
Experiment_132 fold_5	0.6562	0.4155	0.0	0.0	0.0	1.0
Experiment_132

3.2 hmc数据集

简单的先尝试，效果非常的糟糕

name	acc	auc	f1	precision	recall	specificity
Experiment_73 fold_1	0.84375	0.884057	0.888888	0.909090	0.869565	0.777777
Experiment_73 fold_2	0.75	0.744588	0.826086	0.759999	0.904761	0.454545
Experiment_73 fold_3	0.6875	0.647058	0.75	0.652174	0.882352	0.466666
Experiment_73 fold_4	0.6875	0.536796	0.807692	0.677419	1.0	0.090909
Experiment_73 fold_5

3.2.1 mamba+echoprime video

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构

name	acc	auc	f1	precision	recall	specificity
Experiment_85 fold_1	0.90625	0.913043	0.936170	0.916666	0.956521	0.777777
Experiment_85 fold_2	0.78125	0.787878	0.829268	0.85	0.8095	0.7272
Experiment_85 fold_3	0.8125	0.8784	0.8235	0.8235	0.8235	0.8
Experiment_85 fold_4	0.8125	0.8095	0.85	0.8947	0.8095	0.8182
Experiment_85 fold_5	0.84375	0.878787	0.87179	0.9444	0.8095	0.9090
Experiment_85	0.83125

损失改为tversky

name	acc	auc	f1	precision	recall	specificity
Experiment_86 fold_2	0.78125	0.813852	0.820512	0.888888	0.761904	0.818181

损失改为focal

name	acc	auc	f1	precision	recall	specificity
Experiment_87 fold_2	0.78125	0.8	0.8108	0.9375	0.714285	0.9090

损失改为dice

name	acc	auc	f1	precision	recall	specificity
Experiment_88 fold_2	0.78125	0.783549	0.820512	0.888888	0.761904	0.818181

损失改为bce_dice

name	acc	auc	f1	precision	recall	specificity
Experiment_89 fold_2	0.78125	0.805194	0.8205	0.8888	0.7619	0.818181

损失改为asymmetric

name	acc	auc	f1	precision	recall	specificity
Experiment_90 fold_1	0.90625	0.932367	0.936170	0.916666	0.956521	0.777777
Experiment_90 fold_2	0.8125	0.8484	0.8571	0.8571	0.8571	0.7272
Experiment_90 fold_3	0.84375	0.843137	0.864864	0.8	0.941176	0.733333
Experiment_90 fold_4	0.8125	0.835497	0.85	0.894736	0.809523	0.818181
Experiment_90 fold_5	0.84375	0.861471	0.883720	0.863636	0.904761	0.727272
Experiment_90	0.84375	0.864175	0.888351	0.866628	0.891956	0.753332

改为交叉注意力融合

name	acc	auc	f1	precision	recall	specificity
Experiment_95 fold_1	0.84375	0.903381	0.883720	0.949999	0.826086	0.888888
Experiment_95 fold_2	0.78125	0.779220	0.851063	0.769230	0.952380	0.454545
Experiment_95 fold_3	0.8125
Experiment_95 fold_4	0.78125
Experiment_95 fold_5	0.8125	0.818181	0.857142	0.857142	0.857142	0.727272
Experiment_95	拉跨

加权融合

name	acc	auc	f1	precision	recall	specificity
Experiment_96 fold_1	0.90625	0.937198	0.936170	0.916666	0.9565	0.7777
Experiment_96 fold_2	0.8125	0.826839	0.857142	0.857142	0.857142	0.727272
Experiment_96 fold_3	0.8125	0.882352	0.849999	0.739130	1.0	0.6
Experiment_96 fold_4	0.8125	0.826839	0.85	0.894736	0.809523	0.818181
Experiment_96 fold_5	0.84375	0.857142	0.883720	0.863636	0.904761	0.727272
Experiment_96

Adaw优化器

name	acc	auc	f1	precision	recall	specificity
Experiment_97 fold_1	0.90625	0.927536	0.930232	1.0	0.869565	1.0
Experiment_97 fold_2	0.78125	0.831168	0.810810	0.9375	0.714285	0.909090
Experiment_97 fold_3	0.78125	0.882352	0.774193	0.857142	0.705882	0.866666
Experiment_97 fold_4	0.8125	0.844155	0.842105	0.941176	0.761904	0.909090
Experiment_97 fold_5	0.8125	0.883116	0.85	0.894736	0.809523	0.818181
Experiment_97

SGD

name	acc	auc	f1	precision	recall	specificity
Experiment_98 fold_1	0.8125	0.816425	0.869565	0.869565	0.869565	0.666666
Experiment_98 fold_2	0.65625

RMSprop

name	acc	auc	f1	precision	recall	specificity
Experiment_99 fold_1	0.90625	0.913043	0.930232	1.0	0.869565	1.0
Experiment_99 fold_2	0.78125	0.822510	0.810810	0.9375	0.714285	0.909090

Adagrad

name	acc	auc	f1	precision	recall	specificity
Experiment_100 fold_1	0.78125
Experiment_100 fold_2	0.65625

还是改回MADGRAD优化器

做了点数据增强

name	acc	auc	f1	precision	recall	specificity
Experiment_101 fold_1	0.875	0.9565	0.916666	0.879999	0.956521	0.666666
Experiment_101 fold_2	0.8125	0.7922	0.85	0.8947	0.8095	0.818181
Experiment_101 fold_3	0.84375	0.886274	0.871794	0.772727	1.0	0.666666
Experiment_101 fold_4	0.75
Experiment_101 fold_5	0.8125	0.8311	0.875	0.7777	1.0	0.4545

mamba: A2C切面的数据

echo_prime: A4C切面的数据

融合: 通道拼接

保留4*4*4的空间结构

损失还是asymmetric

name	acc	auc	f1	precision	recall	specificity
Experiment_92 fold_1	0.90625	0.971014	0.936170	0.916666	0.956521	0.7777
Experiment_92 fold_2	0.84375	0.805194	0.888888	0.833333	0.952380	0.636363
Experiment_92 fold_3	0.75	0.7960	0.7647	0.7647	0.7647	0.7333
Experiment_92 fold_4	0.78125	0.826839	0.810810	0.9375	0.714285	0.909090
Experiment_92 fold_5	0.84375	0.878787	0.878048	0.899999	0.857142	0.818181
Experiment_92	0.825	效果拉跨

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构

损失还是asymmetric

但是使用的是dec0作为mamba的输出

name	acc	auc	f1	precision	recall	specificity
Experiment_93 fold_1	0.90625	0.942028	0.938775	0.884615	1.0	0.666666
Experiment_93 fold_2	0.8125	0.792207	0.85	0.894736	0.809523	0.818181
Experiment_93 fold_3	0.8125	0.870588	0.823529	0.823529	0.823529	0.8
Experiment_93 fold_4	0.8125	0.805194	0.857142	0.857142	0.857142	0.727272
Experiment_93 fold_5	0.84375	0.874458	0.878048	0.89999	0.857142	0.818181
Experiment_93	0.8375

保留6*6*6的空间结构

name	acc	auc	f1	precision	recall	specificity
Experiment_93 fold_1	0.90625
Experiment_93 fold_3	0.75

mamba: A4C切面的数据

echo_prime: A2C切面的数据

融合: 通道拼接

保留4*4*4的空间结构

损失还是asymmetric

但是使用的是dec2作为mamba的输出

name	acc	auc	f1	precision	recall	specificity
Experiment_94 fold_1	0.90625	0.946859	0.936170	0.916666	0.956521	0.777777
Experiment_94 fold_2	0.78125	0.796536	0.829268	0.85	0.809523	0.727272
Experiment_94 fold_3	0.84375	0.925490	0.848484	0.875	0.823529	0.866666
Experiment_94 fold_4	0.75	0.779220	0.809523	0.809523	0.809523	0.636363
Experiment_94 fold_5	0.8125	0.861471	0.857142	0.857142	0.857142	0.727272

name	acc	auc	f1	precision	recall	specificity
Experiment_95 fold_1 1e-8	0.90625	0.9565	0.938775	0.884615	1.0	0.666666
Experiment_95 fold_3 1e-8	0.8125	0.921568	0.849999	0.739130	1.0	0.6

3.2.2 mamba+echoprime text video

加入知识向量

mamba: A4C切面的数据

echo_prime: A2C切面的数据

都加入了知识向量

融合: 通道拼接

保留4*4*4的空间结构

学习率1e-6 b8 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_116 fold_1	0.90625	0.8067	0.9387	0.8846	1.0	0.6666
Experiment_116 fold_2	0.78125 (0.81)	0.818181	0.8108	0.9375	0.7142	0.9090
Experiment_116 fold_3	0.8125 (0.84)	0.8509	0.8	0.9230	0.7058	0.9333
Experiment_116 fold_4	0.8125	0.8614	0.85	0.8947	0.8095	0.8181
Experiment_116 fold_5	0.84375	0.8484	0.8837	0.8636	0.9047	0.7272
Experiment_116	0.83125

name	acc	auc	f1	precision	recall	specificity
Experiment_127 fold_1	0.90625	0.8792	0.9387	0.8846	1.0	0.6666
Experiment_127 fold_2	0.8125	0.9264	0.8333	1.0	0.7142	1.0
Experiment_127 fold_3	0.8125	0.8666	0.8	0.9230	0.7058	0.9333
Experiment_127 fold_4	0.75	0.8138	0.7894	0.8823	0.7142	0.8181
Experiment_127 fold_5	0.8125	0.8225	0.8571	0.8571	0.8571	0.7272
Experiment_127

学习率1e-6 b16 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_128 fold_1	0.875	0.9178	0.9166	0.8799	0.9565	0.6666
Experiment_128 fold_2	0.75	0.7878	0.7894	0.8823	0.7142	0.8181
Experiment_128 fold_3	0.84375	0.9098	0.8387	0.9285	0.7647	0.9333
Experiment_128 fold_4	0.78125	0.8225	0.8108	0.9375	0.7142	0.9090
Experiment_128 fold_5	0.84375	0.8658	0.8780	0.8999	0.8571	0.8181
Experiment_128	0.81875	0.86072	0.84474	0.90562	0.78136	0.84722

学习率1e-6 b12 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_129 fold_1	0.90625	0.9371	0.9361	0.9166	0.9565	0.7777
Experiment_129 fold_2	0.75	0.7922	0.7999	0.8421	0.7619	0.7272
Experiment_129 fold_3	0.84375	0.8549	0.8387	0.9285	0.7647	0.9333
Experiment_129 fold_4	0.75	0.7965	0.7894	0.8823	0.7142	0.8181
Experiment_129 fold_5	0.84375	0.8614	0.8717	0.9444	0.8095	0.9090
Experiment_129

学习率1e-6 b4 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_130 fold_1	0.90625	0.9516	0.9333	0.9545	0.9130	0.8888
Experiment_130 fold_2	0.78125	0.7835	0.8372	0.8181	0.8571	0.6363
Experiment_130 fold_3	0.84375	0.8784	0.8387	0.9285	0.7647	0.9333
Experiment_130 fold_4	0.78125	0.8	0.8205	0.8888	0.7619	0.8181
Experiment_130 fold_5	0.84375	0.8484	0.8717	0.9444	0.8095	0.9090
Experiment_130

name	acc	auc	f1	precision	recall	specificity
Experiment_131 fold_1	0.9375	0.9565	0.9545	1.0	0.9130	1.0
Experiment_131 fold_2	0.8125	0.8701	0.85	0.8947	0.8095	0.8181
Experiment_131 fold_3	0.875	0.9568	0.8947	0.8095	1.0	0.9333
Experiment_131 fold_4	0.8125	0.8744	0.85	0.8947	0.8095	0.8181
Experiment_131 fold_5	0.875	0.8961	0.8999	0.9473	0.8571	0.9090
Experiment_131	0.8625	0.9108	0.8899	0.9093	0.8778	0.8558

name	acc	auc	f1	precision	recall	specificity
Experiment_131 fold_5 v1	0.84375	0.8831	0.8717	0.9444	0.8095	0.9090
Experiment_131 fold_5 v2	0.84375	0.9047	0.8780	0.8999	0.8571	0.8181

使用dec2作为输出

name	acc	auc	f1	precision	recall	specificity
Experiment_126 fold_1	0.90625	0.8937	0.9387	0.8846	1.0	0.6666
Experiment_126 fold_2	0.8125	0.8571	0.8571	0.8571	0.8571	0.7272
Experiment_126 fold_3	0.75	0.8117	0.75	0.8	0.7058	0.8
Experiment_126 fold_4	0.6875	0.7662	0.75	0.7894	0.7142	0.6363
Experiment_126 fold_5	0.84375	0.8354	0.8718	0.9444	0.8095	0.9090
Experiment_126

学习率1e-5 b8 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_125 fold_1	0.875	0.8743	0.9166	0.8799	0.9565	0.6666
Experiment_125 fold_2	0.75	0.8051	0.7777	0.9333	0.6666	0.9090
Experiment_125 fold_3	0.78125	0.8705	0.7407	1.0	0.5882	1.0
Experiment_125 fold_4	0.71875	0.7445	0.7567	0.875	0.6666	0.8181
Experiment_125 fold_5	0.84375	0.8311	0.8837	0.8636	0.9047	0.7272
Experiment_125

损失：asymmetric

name	acc	auc	f1	precision	recall	specificity
Experiment_117 fold_1	0.90625	0.8405	0.9387	0.8846	1.0	0.6666
Experiment_117 fold_2	0.75	0.7229	0.7777	0.9333	0.6666	0.9090
Experiment_117 fold_3	0.8125 (0.84)	0.8823	0.8421	0.7619	0.9411	0.6666
Experiment_117 fold_4	0.8125	0.8528	0.8421	0.9411	0.7619	0.9090
Experiment_117 fold_5	0.84375	0.8354	0.8837	0.8636	0.9047	0.7272
Experiment_117	0.825

损失weighted_bce 2.0

name	acc	auc	f1	precision	recall	specificity
Experiment_118 fold_1	0.875	0.8937	0.9166	0.8799	0.9565	0.6666
Experiment_118 fold_2	0.78125	0.818181	0.8205	0.8888	0.7619	0.8181
Experiment_118 fold_3	0.8125	0.8431	0.8	0.9230	0.7058	0.9333
Experiment_118 fold_4	0.75	0.8354	0.7894	0.8823	0.7142	0.8181
Experiment_118 fold_5	0.84375	0.8441	0.8837	0.8636	0.9047	0.7272
Experiment_118	0.8125	0.8469	0.8420	0.8875	0.7886	0.7931

损失weighted_bce 0.75

name	acc	auc	f1	precision	recall	specificity
Experiment_119 fold_1	0.90625	0.8550	0.9387	0.8846	1.0	0.6666
Experiment_119 fold_2	0.78125	0.8138	0.8108	0.9375	0.7142	0.9090
Experiment_119 fold_3	0.84375	0.9098	0.8571	0.8333	0.8823	0.8
Experiment_119 fold_4	0.8125	0.8528	0.8571	0.8571	0.8571	0.7272
Experiment_119 fold_5	0.8125	0.8528	0.8571	0.8571	0.8571	0.7272
Experiment_119	0,83125

损失weighted_bce 0.5

name	acc	auc	f1	precision	recall	specificity
Experiment_120 fold_1	0.84375	0.8550	0.8936	0.875	0.9130	0.6666
Experiment_120 fold_2	0.75	0.8268	0.7777	0.9333	0.6666	0.9090
Experiment_120 fold_3	0.875	0.9058	0.8888	0.8421	0.9411	0.8
Experiment_120 fold_4	0.75	0.8398	0.7894	0.8823	0.7142	0.8181
Experiment_120 fold_5	0.78125	0.8398	0.8292	0.85	0.8095	0.7272
Experiment_120	0.8

使用的dec0作为mamba的输出

学习率1e-6 b8 损失：bce

name	acc	auc	f1	precision	recall	specificity
Experiment_121 fold_1	0.875	0.9227	0.9166	0.8799	0.9565	0.6666
Experiment_121 fold_2	0.8125	0.8051	0.875	0.7777	1.0	0.4545
Experiment_121 fold_3	0.875	0.8862	0.8823	0.8823	0.8823	0.8666
Experiment_121 fold_4	0.6875	0.6839	0.7222	0.8666	0.6190	0.8181
Experiment_121 fold_5	0.875	0.8917	0.9047	0.9047	0.9047	0.8181
Experiment_121	0.825

使用的dec0作为mamba的输出

学习率1e-6 b8 损失：asymmetric

name	acc	auc	f1	precision	recall	specificity
Experiment_122 fold_4	0.6875

学习率:1e-4

name	acc	auc	f1	precision	recall	specificity
Experiment_123 fold_1	0.875	0.8550	0.9166	0.8799	0.9565	0.6666
Experiment_123 fold_2	0.75
Experiment_123 fold_3	0.84375
Experiment_123 fold_4	0.71875	0.7532	0.7428	0.9285	0.6190	0.9090
Experiment_123 fold_5	0.8125
Experiment_123

学习率1e-4 b8 损失：bce 少加几层知识向量

name	acc	auc	f1	precision	recall	specificity
Experiment_124 fold_1	0.90625	0.8599	0.9387	0.8846	1.0	0.6666
Experiment_124 fold_2	0.78125	0.8	0.8205	0.8888	0.7619	0.8181
Experiment_124 fold_3	0.84375	0.9568	0.8387	0.9285	0.7647	0.9333
Experiment_124 fold_4	0.71875	0.7532	0.7428	0.9285	0.6190	0.9090
Experiment_124 fold_5	0.8125	0.8311	0.8571	0.8571	0.8571	0.7272
Experiment_124	0.8125

3.3 省医院数据

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_test fold_5

A2C A4C

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_test fold_1	0.75	0.7219	0.8108	0.75	0.8823	0.5454
Experiment_provincial_test fold_2	0.75	0.8556	0.8205	0.7272	0.9411	0.4545
Experiment_provincial_test fold_3	0.8214	0.8214	0.8484	0.7368	1.0	0.6428
Experiment_provincial_test fold_4	0.6666	0.4671	0.7804	0.7272	0.8421	0.25
Experiment_provincial_test fold_5	0.7407	0.6882	0.7878	0.8125	0.7647	0.6999
Experiment_provincial_test	0.74

6切面的数据 mamba

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_1 fold_1	0.8571	0.8877	0.8823	0.8823	0.8823	0.8181
Experiment_provincial_1 fold_2	0.8571	0.9197	0.875	0.9333	0.8235	0.9090
Experiment_provincial_1 fold_3	0.8214	0.8775	0.8275	0.8	0.8571	0.7857
Experiment_provincial_1 fold_4	0.7692	0.5486	0.8571	0.75	1.0	0.25
Experiment_provincial_1 fold_5	0.7307	0.6405	0.7741	0.8571	0.7058	0.7777
Experiment_provincial_1	0.8088	0.7881	0.8452	0.8353	0.8554	0.7358

6切面的数据 mamba 三分类

name	acc	auc	f1	precision	recall
Experiment_provincial_3 fold_1	0.75	0.6893	0.75	0.75	0.75
Experiment_provincial_3 fold_2	0.7142	0.8654	0.7142	0.7142	0.7142
Experiment_provincial_3 fold_3	0.75	0.8795	0.75	0.75	0.75
Experiment_provincial_3 fold_4	0.8076	0.6731	0.8076	0.8076	0.8076
Experiment_provincial_3 fold_5	0.7307	0.6515	0.7307	0.7307	0.7307
Experiment_provincial_3	0.7505	0.7518	0.7505	0.7505	0.7505

430例

name	acc	auc	f1	precision	recall
Experiment_provincial_15 fold_0	0.5909	0.6442	0.5909	0.5909	0.5909
Experiment_provincial_15 fold_1	0.6351	0.5633	0.6351	0.6351	0.6351
Experiment_provincial_15 fold_2	0.6279	0.4458	0.6279	0.6279	0.6279
Experiment_provincial_15 fold_3	0.6304	0.4827	0.6304	0.6304	0.6304
Experiment_provincial_15 fold_4	0.5595	0.5470	0.5595	0.5595	0.5595
Experiment_provincial_15

name	acc	auc	f1	precision	recall
Experiment_provincial_16 fold_0	0.6022	0.6275	0.6022	0.6022	0.6022
Experiment_provincial_16 fold_1	0.6351	0.5748	0.6351	0.6351	0.6351
Experiment_provincial_16 fold_2	0.6395	0.5086	0.6395	0.6395	0.6395
Experiment_provincial_16 fold_3	0.6413	0.4055	0.6413	0.6413	0.6413
Experiment_provincial_16 fold_4	0.6071	0.6992	0.6071	0.6071	0.6071
Experiment_provincial_16	0.6250	0.5631	0.6250	0.6250	0.6250

6切面的数据 echoprime 32F

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_2 fold_1	0.8125	0.9047	0.8571	0.75	1.0	0.5714
Experiment_provincial_2 fold_2	0.8125	0.6727	0.8799	0.7857	1.0	0.4
Experiment_provincial_2 fold_3	0.8125	0.8166	0.8	0.6666	1.0	0.6999
Experiment_provincial_2 fold_4	0.8125	0.5	0.8888	0.8	1.0	0.25
Experiment_provincial_2 fold_5	0.625	0.55	0.7692	0.625	1.0	0.0
Experiment_provincial_2

加上12月的数据

六切面 16F

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_4 fold_1	0.75	0.7366	0.8346	0.7361	0.9636	0.3448
Experiment_provincial_4 fold_2	0.7093	0.7020	0.7788	0.7097	0.8627	0.4857
Experiment_provincial_4 fold_3	0.7262	0.7312	0.7473	0.7234	0.7727	0.6750
Experiment_provincial_4 fold_4	0.6860	0.6577	0.7652	0.6769	0.8799	0.4166
Experiment_provincial_4 fold_5	0.7143	0.6695	0.7931	0.7302	0.8679	0.4516
Experiment_provincial_4

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_13 fold_1	0.7857	0.8012	0.8448	0.8032	0.8909	0.5862
Experiment_provincial_13 fold_2	0.6744	0.6224	0.7666	0.6666	0.9020	0.3428
Experiment_provincial_13 fold_3	0.75	0.7540	0.7470	0.7949	0.7045	0.8
Experiment_provincial_13 fold_4	0.6860	0.6277	0.7768	0.6620	0.9399	0.3333
Experiment_provincial_13 fold_5	0.6904	0.6524	0.7592	0.7454	0.7736	0.5484
Experiment_provincial_13	0.7173

新分折

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_14 fold_0	0.6931	0.6963	0.7216	0.7609	0.6862	0.7027
Experiment_provincial_14 fold_1	0.7567	0.7720	0.7954	0.7954	0.7954	0.6999
Experiment_provincial_14 fold_2	0.7209	0.7020	0.7857	0.7586	0.8148	0.5625
Experiment_provincial_14 fold_3	0.6739	0.7306	0.6875	0.8461	0.5789	0.8286
Experiment_provincial_14 fold_4	0.5952	0.5399	0.6909	0.6129	0.7917	0.3333
Experiment_provincial_14	0.6880

A2C + A4C

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_9 fold_1	0.8	0.8026	0.8491	0.8181	0.8824	0.6552
Experiment_provincial_9 fold_2	0.7250	0.7104	0.7755	0.7169	0.8444	0.5714
Experiment_provincial_9 fold_3	0.75	0.7654	0.7727	0.7234	0.8293	0.6666
Experiment_provincial_9 fold_4	0.75	0.7644	0.7561	0.8378	0.6888	0.8286
Experiment_provincial_9 fold_5	0.7125	0.6466	0.7965	0.7143	0.8999	0.4
Experiment_provincial_9	0.7475	0.7379	0.79	0.7621	0.8290	0.6244

新分折

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_10 fold_0	0.6704
Experiment_provincial_10 fold_1	0.6527
Experiment_provincial_10 fold_2	0.725
Experiment_provincial_10 fold_3
Experiment_provincial_10 fold_4
Experiment_provincial_10

A2C + A3C + A4C

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_11 fold_1	0.8095	0.8508	0.8545	0.8545	0.8545	0.7241
Experiment_provincial_11 fold_2	0.7381	0.7038	0.7924	0.7368	0.8571	0.5714
Experiment_provincial_11 fold_3	0.75	0.7897	0.7640	0.7555	0.7727	0.725
Experiment_provincial_11 fold_4	0.75	0.7481	0.7921	0.7692	0.8163	0.6571
Experiment_provincial_11 fold_5	0.7381	0.6962	0.8	0.7719	0.8301	0.5806
Experiment_provincial_11	0.7571	0.7577	0.8006	0.7776	0.8262	0.6517

AP SAX + MV SAX + PM SAX

name	acc	auc	f1	precision	recall	specificity
Experiment_provincial_12 fold_1	0.7976	0.8132	0.8468	0.8393	0.8545	0.6896
Experiment_provincial_12 fold_2	0.7619	0.7738	0.8	0.7843	0.8163	0.6857
Experiment_provincial_12 fold_3	0.7857	0.8159	0.7857	0.8250	0.75	0.8250
Experiment_provincial_12 fold_4	0.7619	0.7796	0.8113	0.7544	0.8775	0.60
Experiment_provincial_12 fold_5	0.7381	0.7553	0.7924	0.7924	0.7924	0.6451
Experiment_provincial_12	0.7690	0.7876	0.8073	0.7991	0.8182	0.6891

4. 模型结构图

5. sota实验

5.1 camus

name	acc	auc	f1	precision	recall	specificity
BI-Mamba	78.75	73.45	64.87	85.01	54.85	/
CARL	83.75	60.33	73.42	85.56	67.42	/
MV-Swin-T	62.50	57.14	60.42	61.65	62.17	/
CTT-Net	62.50	70.04	70.70	62.96	82.62	/
MIMamba	85.00	83.15	77.78	84.00	72.41	92.16

5.2 hmc

name	acc	auc	f1	precision	recall	specificity
SAF-Net	78.13	\	81.57	88.26	77.64	/
BI-Mamba	77.50	73.07	83.85	78.12	91.59	/
CARL	85.62	45.56	87.30	86.43	93.13	/
MV-Swin-T	75.63	80.62	76.82	70.63	73.83	/
CTT-Net	72.50	78.39	71.38	73.54	71.00	/
LVSnake	83.09	\	87.17	86.49	90.11	/
MIMamba	86.25	91.08	88.99	90.93	87.78	85.58

6. 单切面对比实验

6.1 camus

汇总

view	name	acc	auc	f1	precision	recall
A4C	logvmamba	0.7625	0.7069	0.5456	0.8605	0.4575
	lkm_unet	0.7875	0.7537	0.5917	0.8466	0.5424
	ukan3d	0.7125	0.7374	0.6782	0.5837	0.8257
	dk-mamba	0.8063	0.7838	0.6882	0.7962	0.6181
A2C	logvmamba	0.7375	0.6521	0.5873	0.7558	0.5161
	lkm_unet	0.7250	0.7202	0.6595	0.6790	0.7454
	ukan3d	0.7063	0.7040	0.6351	0.5832	0.7060
	dk-mamba	0.7750	0.7790	0.6834	0.6880	0.6848

A4C切面

name	acc	auc	f1	precision	recall	specificity
logvmamba	0.7625	0.7069	0.5456	0.8605	0.4575	0.9299
lkm_unet	0.7875	0.7537	0.5917	0.8466	0.5424	0.9184
ukan3d	0.7125	0.7374	0.6782	0.5837	0.8257	0.6495
dk-mamba	0.8063	0.7838	0.6882	0.7962	0.6181	0.9118

dk-mamba

name	acc	auc	f1	precision	recall	specificity
Experiment_138 fold_1	0.78125	0.6999	0.6666	0.7777	0.5833	0.8999
Experiment_138 fold_2	0.78125	0.75	0.6315	0.8571	0.5	0.9499
Experiment_138 fold_3	0.90625	0.925	0.8799	0.8461	0.9166	0.8999
Experiment_138 fold_4	0.78125	0.8311	0.6315	0.75	0.5454	0.9047
Experiment_138 fold_5	0.78125	0.7142	0.6315	0.75	0.5454	0.9047
Experiment_138	0.8063	0.7838	0.6882	0.7962	0.6181	0.9118

logvmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_133 fold_1	0.75	0.7250	0.6363	0.6999	0.5833	0.85
Experiment_133 fold_2	0.75	0.5833	0.5555	0.8333	0.4166	0.9499
Experiment_133 fold_3	0.84375	0.8542	0.8	0.7692	0.8333	0.85
Experiment_133 fold_4	0.75	0.6883	0.4285	1.0	0.2727	1.0
Experiment_133 fold_5	0.71875	0.6839	0.3076	1.0	0.1818	1.0
Experiment_133	0.7625	0.7069	0.5456	0.8605	0.4575	0.9299

lkm_unet

name	acc	auc	f1	precision	recall	specificity
Experiment_135 fold_1	0.875	0.8833	0.8333	0.8333	0.8333	0.8999
Experiment_135 fold_2	0.75	0.7624	0.5555	0.8333	0.4166	0.9499
Experiment_135 fold_3	0.84375	0.85	0.8148	0.7333	0.9166	0.8
Experiment_135 fold_4	0.78125	0.7273	0.5882	0.8333	0.4545	0.9523
Experiment_135 fold_5	0.6875	0.5454	0.1666	1.0	0.0909	1.0
Experiment_135	0.7875	0.7537	0.5917	0.8466	0.5424	0.9184

ukan3d

name	acc	auc	f1	precision	recall	specificity
Experiment_136 fold_1	0.75	0.8167	0.7333	0.6111	0.9167	0.6499
Experiment_136 fold_2	0.7187	0.6833	0.64	0.6153	0.6666	0.75
Experiment_136 fold_3	0.875	0.8708	0.8571	0.75	1.0	0.8
Experiment_136 fold_4	0.6875	0.7532	0.6153	0.5333	0.7272	0.6666
Experiment_136 fold_5	0.53125	0.5628	0.5454	0.4090	0.8182	0.3809
Experiment_136	0.7125	0.7374	0.6782	0.5837	0.8257	0.6495

segformer

name	acc	auc	f1	precision	recall	specificity
Experiment_137 fold_1
Experiment_137 fold_2	0.6875	0.7291	0.6666	0.5555	0.8333	0.6
Experiment_137 fold_3
Experiment_137 fold_4
Experiment_137 fold_5
Experiment_137

A2C切面

name	acc	auc	f1	precision	recall	specificity
logvmamba	0.7375	0.6521	0.5873	0.7558	0.5161	0.8629
lkm_unet	0.7250	0.7202	0.6595	0.6790	0.7454	0.7195
ukan3d	0.7063	0.7040	0.6351	0.5832	0.7060	0.7071
dk-mamba	0.7750	0.7790	0.6834	0.6880	0.6848	0.8233

dk-mamba

name	acc	auc	f1	precision	recall	specificity
Experiment_141 fold_1	0.875	0.85	0.8462	0.7857	0.9166	0.85
Experiment_141 fold_2	0.8125	0.8042	0.75	0.75	0.75	0.85
Experiment_141 fold_3	0.71875	0.7125	0.64	0.6153	0.6666	0.75
Experiment_141 fold_4	0.875	0.8268	0.8	0.8888	0.7273	0.9523
Experiment_141 fold_5	0.59375	0.7013	0.3809	0.4	0.3636	0.7142
Experiment_141	0.7750	0.7790	0.6834	0.6880	0.6848	0.8233

logvmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_134 fold_1	0.6875	0.7292	0.5	0.625	0.4166	0.85
Experiment_134 fold_2	0.78125	0.6083	0.5882	1.0	0.4166	1.0
Experiment_134 fold_3	0.71875	0.6375	0.64	0.6154	0.6666	0.75
Experiment_134 fold_4	0.8125	0.5974	0.625	1.0	0.4545	1.0
Experiment_134 fold_5	0.6875	0.6883	0.5833	0.5385	0.6363	0.7143
Experiment_134	0.7375	0.6521	0.5873	0.7558	0.5161	0.8629

lkm_unet

name	acc	auc	f1	precision	recall	specificity
Experiment_139 fold_1	0.8125	0.7791	0.7272	0.8	0.6666	0.8999
Experiment_139 fold_2	0.75	0.7166	0.7333	0.6111	0.9166	0.6499
Experiment_139 fold_3	0.78125	0.7458	0.5882	1.0	0.4166	1.0
Experiment_139 fold_4	0.59375	0.7186	0.6060	0.4545	0.9090	0.4285
Experiment_139 fold_5	0.6875	0.6407	0.6428	0.5294	0.8181	0.6190
Experiment_139	0.7250	0.7202	0.6595	0.6790	0.7454	0.7195

ukan3d

name	acc	auc	f1	precision	recall	specificity
Experiment_140 fold_1	0.71875	0.6833	0.64	0.6154	0.6666	0.75
Experiment_140 fold_2	0.75	0.7583	0.7142	0.625	0.8333	0.6999
Experiment_140 fold_3	0.75	0.7625	0.6666	0.6666	0.6666	0.8
Experiment_140 fold_4	0.625	0.6883	0.5714	0.4705	0.7272	0.5714
Experiment_140 fold_5	0.6875	0.6277	0.5833	0.5384	0.6363	0.7143
Experiment_140	0.7063	0.7040	0.6351	0.5832	0.7060	0.7071

6.2 hmc

汇总

view	name	acc	auc	f1	precision	recall
A4C	logvmamba	0.7438	0.6974	0.8070	0.7626	0.8652
	lkm_unet	0.7875	0.7640	0.8520	0.7642	0.9669
	ukan3d	0.7938	0.7951	0.8569	0.7723	0.9692
	dkmamba	0.8313	0.8728	0.8625	0.8899	0.8395
A2C	logvmamba	0.75	0.69148	0.81084	0.77048	0.86518
	lkm_unet	0.75	0.7635	0.8220	0.7507	0.9114
	ukan3d	0.7563	0.7538	0.8346	0.75222	0.9540
	dkmamba	0.825	0.8574	0.8534	0.8534	0.8039

A4C切面

name	acc	auc	f1	precision	recall	specificity
logvmamba	0.7438	0.6974	0.8070	0.7626	0.8652	0.5175
lkm_unet	0.7875	0.7640	0.8520	0.7642	0.9669	0.4589
ukan3d	0.7938	0.7951	0.8569	0.7723	0.9692	0.4771
dkmamba	0.8313	0.8728	0.8625	0.8899	0.8395	0.7967

dkmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_147 fold_1	0.90625	0.9468	0.9362	0.9167	0.9565	0.7777
Experiment_147 fold_2	0.84375	0.9134	0.8780	0.8999	0.8571	0.8181
Experiment_147 fold_3	0.84375	0.9020	0.8387	0.9285	0.7647	0.9333
Experiment_147 fold_4	0.75	0.7705	0.8095	0.8095	0.8095	0.6363
Experiment_147 fold_5	0.8125	0.8311	0.85	0.8947	0.8095	0.8181
Experiment_147	0.8313	0.8728	0.8625	0.8899	0.8395	0.7967

logvmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_148 fold_1	0.8125	0.9082	0.8695	0.8695	0.8695	0.6666
Experiment_148 fold_2	0.875	0.8311	0.9047	0.9047	0.9047	0.8181
Experiment_148 fold_3	0.5625	0.4666	0.6111	0.5789	0.6470	0.4666
Experiment_148 fold_4	0.75	0.6320	0.8260	0.7599	0.9047	0.4545
Experiment_148 fold_5	0.71875	0.6493	0.8235	0.6999	1.0	0.1818
Experiment_148	0.7438	0.6974	0.8070	0.7626	0.8652	0.5175

lkm_unet

name	acc	auc	f1	precision	recall	specificity
Experiment_149 fold_1	0.875	0.8309	0.92	0.8518	1.0	0.5555
Experiment_149 fold_2	0.875	0.8268	0.9130	0.8399	1.0	0.6363
Experiment_149 fold_3	0.6875	0.7294	0.75	0.6521	0.8823	0.4666
Experiment_149 fold_4	0.6875	0.6017	0.8077	0.6774	1.0	0.0909
Experiment_149 fold_5	0.8125	0.8311	0.8695	0.8	0.9523	0.5454
Experiment_149	0.7875	0.7640	0.8520	0.7642	0.9669	0.4589

ukan3d

name	acc	auc	f1	precision	recall	specificity
Experiment_146 fold_1	0.875	0.9565	0.92	0.8518	1.0	0.5555
Experiment_146 fold_2	0.875	0.8398	0.9130	0.8399	1.0	0.6363
Experiment_146 fold_3	0.71875	0.7333	0.7804	0.6666	0.9411	0.4666
Experiment_146 fold_4	0.6875	0.5974	0.8077	0.6774	1.0	0.0909
Experiment_146 fold_5	0.8125	0.8484	0.8636	0.8260	0.9047	0.6363
Experiment_146	0.7938	0.7951	0.8569	0.7723	0.9692	0.4771

A2C切面

name	acc	auc	f1	precision	recall	specificity
logvmamba	0.75	0.69148	0.81084	0.77048	0.86518	0.53974
lkm_unet	0.75	0.7635	0.8220	0.7507	0.9114	0.4598
ukan3d	0.7563	0.7538	0.8346	0.75222	0.9540	0.4311
dkmamba	0.825	0.8574	0.8534	0.8534	0.8039	0.8553

dkmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_145 fold_1	0.84375	0.8792	0.8837	0.9499	0.8261	0.8888
Experiment_145 fold_2	0.78125	0.8009	0.8108	0.9375	0.7143	0.9090
Experiment_145 fold_3	0.84375	0.9058	0.8387	0.9285	0.7647	0.9333
Experiment_145 fold_4	0.78125	0.8052	0.8292	0.85	0.8095	0.7272
Experiment_145 fold_5	0.875	0.8961	0.9047	0.9047	0.9047	0.8181
Experiment_145	0.825	0.8574	0.8534	0.8534	0.8039	0.8553

logvmamba

name	acc	auc	f1	precision	recall	specificity
Experiment_142 fold_1	0.84375	0.9082	0.8888	0.9090	0.8695	0.7777
Experiment_142 fold_2	0.875	0.8311	0.9047	0.9047	0.9047	0.8181
Experiment_142 fold_3	0.5625	0.4627	0.6111	0.5789	0.6470	0.4666
Experiment_142 fold_4	0.75	0.6277	0.8261	0.7599	0.9047	0.4545
Experiment_142 fold_5	0.71875	0.6277	0.8235	0.6999	1.0	0.1818
Experiment_142	0.75	0.69148	0.81084	0.77048	0.86518	0.53974

lkm_unet

name	acc	auc	f1	precision	recall	specificity
Experiment_143 fold_1	0.78125	0.7874	0.8571	0.8076	0.9130	0.4444
Experiment_143 fold_2	0.78125	0.7748	0.8444	0.7916	0.9047	0.5454
Experiment_143 fold_3	0.65625	0.6235	0.7317	0.625	0.8823	0.4
Experiment_143 fold_4	0.78125	0.8181	0.8510	0.7692	0.9523	0.4545
Experiment_143 fold_5	0.75	0.8138	0.8260	0.7599	0.9047	0.4545
Experiment_143	0.75	0.7635	0.8220	0.7507	0.9114	0.4598

ukan3d

name	acc	auc	f1	precision	recall	specificity
Experiment_144 fold_1	0.90625	0.8695	0.9333	0.9545	0.9130	0.8888
Experiment_144 fold_2	0.78125	0.7922	0.8510	0.7692	0.9523	0.4545
Experiment_144 fold_3	0.65625	0.7176	0.7555	0.6071	1.0	0.2666
Experiment_144 fold_4	0.6875	0.5670	0.7999	0.6896	0.9523	0.1818
Experiment_144 fold_5	0.75	0.8225	0.8333	0.7407	0.9523	0.3636
Experiment_144	0.7563	0.7538	0.8346	0.75222	0.9540	0.4311

7. 消融实验

7.1 camus

语义校准二腔切面补充

name	acc	auc	f1	precision	recall	specificity
-语义校准 -二腔切面	80.00	76.98	66.13	83.45	58.87	91.14
+语义校准 -二腔切面	83.13	85.91	75.99	77.93	75.90	87.23
-语义校准 +二腔切面	81.88	83.04	73.23	76.83	70.6	88.19
+语义校准 +二腔切面	85.00	83.15	77.78	84.00	72.41	92.16

-语义校准 -二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_150 fold_1	0.875	0.8458	0.8333	0.8333	0.8333	0.8999
Experiment_150 fold_2	0.71875	0.7666	0.4	1.0	0.25	1.0
Experiment_150 fold_3	0.8125	0.8083	0.7692	0.7142	0.8333	0.8
Experiment_150 fold_4	0.875	0.8095	0.7777	1.0	0.6363	1.0
Experiment_150 fold_5	0.71875	0.6190	0.5263	0.625	0.4545	0.8571
Experiment_150	0.8	0.7698	0.6613	0.8345	0.5887	0.9114

+语义校准 -二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_151 fold_1	0.90625	0.9666	0.8695	0.9090	0.8333	0.9499
Experiment_151 fold_2	0.75	0.7375	0.6	0.75	0.5	0.8999
Experiment_151 fold_3	0.8125	0.8208	0.7857	0.6875	0.9166	0.75
Experiment_151 fold_4	0.84375	0.8398	0.7826	0.75	0.8181	0.8571
Experiment_151 fold_5	0.84375	0.9307	0.7619	0.8	0.7272	0.9047
Experiment_151	0.83125	0.85908	0.75994	0.77930	0.75904	0.87232

-语义校准 +二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_152 fold_1	0.90625	0.9	0.8799	0.8461	0.9166	0.8999
Experiment_152 fold_2	0.75	0.7625	0.6363	0.6999	0.5833	0.85
Experiment_152 fold_3	0.78125	0.7666	0.6956	0.7272	0.6666	0.85
Experiment_152 fold_4	0.875	0.8658	0.8181	0.8181	0.8181	0.9047
Experiment_152 fold_5	0.78125	0.8571	0.6315	0.75	0.5454	0.9047
Experiment_152	0.81875	0.8304	0.73228	0.76826	0.706	0.88186

7.2 hmc

name	acc	auc	f1	precision	recall	specificity
-语义校准 -二腔切面	81.25	80.17	85.32	84.98	86.83	73.41
+语义校准 -二腔切面	85.62	87.37	88.52	90.44	87.20	81.98
-语义校准 +二腔切面	82.50	86.54	84.51	94.60	77.08	90.51
+语义校准 +二腔切面	86.25	91.08	88.99	90.93	87.78	85.58

-语义校准 -二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_153 fold_1	0.90625	0.9468	0.9333	0.9545	0.9130	0.8888
Experiment_153 fold_2	0.84375	0.7878	0.8837	0.8636	0.9047	0.7272
Experiment_153 fold_3	0.8125	0.8196	0.8499	0.7391	1.0	0.60
Experiment_153 fold_4	0.75	0.7229	0.8095	0.8095	0.8095	0.6363
Experiment_153 fold_5	0.75	0.7316	0.7895	0.8824	0.7142	0.8181
Experiment_153	0.8125	0.80174	0.85318	0.84982	0.86828	0.73408

+语义校准 -二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_154 fold_1	0.84375	0.8502	0.8888	0.9090	0.8695	0.7777
Experiment_154 fold_2	0.84375	0.9004	0.8888	0.8333	0.9523	0.6363
Experiment_154 fold_3	0.84375	0.8470	0.8484	0.875	0.8235	0.8666
Experiment_154 fold_4	0.875	0.9177	0.8947	1.0	0.8095	1.0
Experiment_154 fold_5	0.875	0.8528	0.9047	0.9047	0.9047	0.8181
Experiment_154	0.8562	0.8737	0.8852	0.9044	0.8720	0.8198

-语义校准 +二腔切面

name	acc	auc	f1	precision	recall	specificity
Experiment_155 fold_1	0.84375	0.8985	0.8837	0.9499	0.8260	0.8888
Experiment_155 fold_2	0.71875	0.7792	0.7567	0.875	0.6666	0.8181
Experiment_155 fold_3	0.8125	0.8745	0.7857	1.0	0.6470	1.0
Experiment_155 fold_4	0.875	0.9177	0.8947	1.0	0.8095	1.0
Experiment_155 fold_5	0.875	0.8571	0.9047	0.9047	0.9047	0.8181
Experiment_155	0.8250	0.8654	0.8451	0.9460	0.7708	0.9051

8. 关于写作

8.1 创新点1

利用文本对记忆的多层特征进行校准Mamba 选择性记忆与语义门控的“分层协同精调”

KnowledgeFusion 作用在多个尺度上。

表述逻辑： Mamba 负责在时空维度上“决定记录什么”，而文本向量负责在语义维度上“决定纠正什么”。
创新描述： 模型将 Mamba 的**长程选择性扫描能力（Long-range Selective Scan）与多级语义门控融合（Gated Fusion）**相结合。Mamba 编码器在底层利用 $S6$ 算法自动捕捉视频中的解剖结构运动，而 Decoder 阶段通过注入文本知识向量，对 Mamba 提取的抽象状态进行“语义校准”。
机制深度： 这种“先扫描、后校准”的策略，利用了文本向量作为语义锚点，对 Mamba 可能遗漏的关键病灶特征（如特定的室壁运动异常）进行增强，对扫描过程中的成像伪影进行抑制

8.2 创新点2

多视图 双路径时空特征与临床语义的“高阶关联增强”

表述逻辑： 融合了“经过 Mamba 压缩的时空特征”和“经过文本精调的视觉特征”。
创新描述： 不同于简单的单流网络，本模型构建了双路径学习框架。路径 A 利用 Mamba 的选择性状态空间有效地压缩了高维度的 A4C 视频序列；路径 B 利用 EchoPrime 编码器实现了文本引导的 A2C 特征精调。
学术意义： 通过 ClsMLP 实现两者的非线性融合，模型实际上是在执行一种临床决策仿真：既参考了 Mamba 捕捉到的动态物理运动（Spatio-temporal selection），又参考了临床文本定义的解剖学逻辑（Semantic refinement），显著提升了二分类任务的稳健性。

Unlike previous Transformer-based methods that suffer from quadratic complexity, our model leverages the Selective State Space (Mamba) to efficiently capture dynamic cardiac motions. The core innovation lies in a Text-Guided Refinement strategy: we utilize cross-modal similarity to activate relevant clinical priors, which subsequently act as semantic governors to refine the selective memory states of the Mamba backbone at multiple scales.

医学影像

#深度学习 #论文 #分类 #mamba

mamba心脏疾病诊断

http://example.com/2025/09/25/mamba-classify/

作者

Mercury

发布于

2025年9月25日

许可协议

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