论文标题
深入研究医学图像分析的自我监督学习：数据，模型和任务
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks
论文作者
论文摘要
自我监督的学习（SSL）通过大量未标记数据的先知在各种医学成像任务中取得了出色的表现。但是，关于一项特定的下游任务，仍然缺乏有关如何在整个标准``preftrain-then-Finetune''工作流程中选择合适的借口任务和实现细节的指令书。在这项工作中，我们专注于以四个现实且重大的问题来利用SSL的能力：（1）SSL对不平衡数据集的影响，（2）网络体系结构，（3）上游任务在下游任务中的适用性以及（4）SSL和常见政策的堆叠效应和常见的学习效果。我们通过有关预测性，对比，生成和多SSL算法的广泛实验提供了大规模，深入和细粒度的研究。根据结果，我们发现了一些见解。很积极，SSL主要通过促进稀有类的表现，这是临床诊断感兴趣的。不幸的是，在某些情况下，SSL提供了边际甚至负回报，包括严重失衡和相对平衡的数据制度，以及与常见培训政策的结合。我们有趣的发现为在医学环境中使用SSL提供了实用的指南，并强调需要开发通用借口任务以适应各种应用程序方案。
Self-supervised learning (SSL) has achieved remarkable performance in various medical imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific downstream task, there is still a lack of an instruction book on how to select suitable pretext tasks and implementation details throughout the standard ``pretrain-then-finetune'' workflow. In this work, we focus on exploiting the capacity of SSL in terms of four realistic and significant issues: (1) the impact of SSL on imbalanced datasets, (2) the network architecture, (3) the applicability of upstream tasks to downstream tasks and (4) the stacking effect of SSL and common policies for deep learning. We provide a large-scale, in-depth and fine-grained study through extensive experiments on predictive, contrastive, generative and multi-SSL algorithms. Based on the results, we have uncovered several insights. Positively, SSL advances class-imbalanced learning mainly by boosting the performance of the rare class, which is of interest to clinical diagnosis. Unfortunately, SSL offers marginal or even negative returns in some cases, including severely imbalanced and relatively balanced data regimes, as well as combinations with common training policies. Our intriguing findings provide practical guidelines for the usage of SSL in the medical context and highlight the need for developing universal pretext tasks to accommodate diverse application scenarios.
