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

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

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

3.2 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	1
Experiment_127 fold_2
Experiment_127 fold_3
Experiment_127 fold_4
Experiment_127 fold_5
Experiment_127

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

4. sota实验

4.1 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

4.2 hmc

5. 示意图

Exp_115可视化ROC