# MUSCLE - MICCAI 2022 这是一篇论文 "MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts" 的相关介绍。该论文发布于MICCAI 2022。 ## 简介 MUSCLE的主标是通过预训练一个主干网络，来提高深度学习在医学影像分析任务中的性能。该论文的所有代码均使用PaddlePaddle框架实现。 ## 框架 ![image](https://user-images.githubusercontent.com/50255927/189317770-c8c9e866-beb2-4eb5-8116-21ab00850ef0.png) MUSCLE聚合了从不同人体部位收集的多个Xray图像数据集，并作用于各种Xray影像的分析任务。我们提出了多数据集动量对比表征学习（MD-MoCo）模块和多任务持续学习模块，以自我监督的持续学习方式对深度学习框架的主干网络进行预训练。预训练的模型可以使用特定任务的head对目标任务进行微调，并取得极佳的性能。 ## 数据集

Datasets	Body Part	Task	Train	Valid	Test	Total
Only Used for the first step (MD-MoCo) of MUSCLE
NIHCC	Chest	N/A	112,120	N/A	N/A	112,120
China-Set-CXR	Chest	N/A	661	N/A	N/A	661
Montgomery-Set-CXR	Chest	N/A	138	N/A	N/A	138
Indiana-CXR	Chest	N/A	7,470	N/A	N/A	7,470
RSNA Bone Age	Hand	N/A	10,811	N/A	N/A	10,811
Used for all three steps of MUSCLE
Pneumonia	Chest	Classification	4,686	585	585	5,856
MURA	Various Bone	Classification	32,013	3,997	3,997	40,005
Chest Xray Masks and labels	Chest	Segmentation	718	89	89	896
TBX	Chest	Detection	640	80	80	800
Total	N/A	N/A	169,257	4,751	4,479	178,757

## 实验 ### 实验设置 - 主干网络 - ResNet-18、 ResNet-50 - 医学影像分析任务 - 肺炎分类任务 (Pneumonia), - 骨骼异常分类任务 (MURA) - 肺部分割任务 (Lung) - 结核病Bounding Box检测 (TBX) - Head网络 - 分类任务：Fully-Connected (FC) Layer - 分割任务：DeepLab-V3 - 检测任务：FasterRCNN - 基线的预训练算法 - **Scratch**: 模型主干网络使用Kaiming’s initialization进行参数初始化 - **ImageNet**: 模型主干网络使用官方发布的ImageNet进行参数初始化 - **MD-MoCo**: 模型主干网络只使用在多数据源的Xray图像进行MoCo学习的参数进行初始化 - **MUSCLE−−**: 模型的初始化策略和MUSCLE一致，但是不采用我们设计的跨任务记忆与循环和重组学习计划模块 ### 不同身体部位的Xray数据集的结果注意：Pneumonia是由**胸片**图像构成的数据集，而MURA由**骨骼**图像构成

Datasets	Backbones	Pre-train	Acc.	Sen.	Spe.	AUC(95%CI)
Pneumonia	ResNet-18	Scratch	91.11	93.91	83.54	96.58(95.09-97.81)
		ImageNet	90.09	93.68	80.38	96.05(94.24-97.33)
		MD-MoCo	96.58	97.19	94.94	98.48(97.14-99.30)
		MUSCLE--	96.75	97.66	94.30	99.51(99.16-99.77)
		MUSCLE	97.26	97.42	96.84	99.61(99.32-99.83)
	ResNet-50	Scratch	91.45	92.51	88.61	96.55(95.08-97.82)
		ImageNet	95.38	95.78	94.30	98.72(98.03-99.33)
		MD-MoCo	97.09	98.83	92.41	99.53(99.23-99.75)
		MUSCLE--	96.75	98.36	92.41	99.58(99.30-99.84)
		MUSCLE	98.12	98.36	97.47	99.72(99.46-99.92)
MURA	ResNet-18	Scratch	81.00	68.17	89.91	86.62(85.73-87.55)
		ImageNet	81.88	73.49	87.70	88.11(87.18-89.03)
		MD-MoCo	82.48	72.27	89,57	88.28(87.28-89.26)
		MUSCLE--	82.45	74.16	88.21	88.41(87.54-89.26)
		MUSCLE	82.62	74.28	88.42	88.5o(87.46-89.57)
	RcsNet-50	Scratch	80.50	65.42	90.97	86.22(85.22-87.35)
		ImngeNet	81.73	68.36	91.01	87.87(86.85-88.85)
		MD-MoCo	82.35	73.12	88.76	87.89(87.06-88.88)
		MUSCLE--	81.10	69.03	89.48	87.14(86.10-88.22)
		MUSCLE	82.60	74.53	88.21	88.37(87.38-89.32)

![image](https://user-images.githubusercontent.com/50255927/189317679-e3c22309-899b-4f8f-a689-d81e406376b5.png) ### 不同任务的结果注意：Lung为肺部**分割**任务，而TBX为**检测**任务

Backbones	Pre-train	Lung		TBX
Backbones	Pre-train	Dice	mloU	mAP	AP-Active	AP-Latent
ResNet-18	Scratch	95.24	94.00	30.71	56.71	4.72
	ImageNet	95.26	94.10	29.46	56.27	2.66
	MD-MoCo	95.31	94.14	36.00	67.17	4.84
	MUSCLE--	95.14	93.90	34.70	63.43	5.97
	MUSCLE	95.37	94.22	36.71	64.84	8.59
ResNet-50	Scratch	93.52	92.03	23.93	44.85	3.01
	ImageNet	93.77	92.43	35.61	58.81	12.42
	MD-MoCo	94.33	93.04	36.78	64.37	9.18
	MUSCLE--	95.04	93.82	35.14	57.32	12.97
	MUSCLE	95.27	94.10	37.83	63.46	12.21

![image](https://user-images.githubusercontent.com/50255927/189317479-14ecb3de-da80-4df3-b9a0-f1fece7b953f.png) ## Citation 如果我们的项目在学术上帮助到你，请考虑以下引用： ``` @inproceedings{liao2022muscle, title={MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts}, author={Weibin, Liao and Haoyi, Xiong and Qingzhong, Wang and Yan, Mo and Xuhong, Li and Yi, Liu and Zeyu, Chen and Siyu, Huang and Dejing, Dou}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} } ```