Large-scale benchmarking and boosting transfer learning for medical image analysis

Abstract

Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using different strategies. But, there is a lack of up-to-date large-scale evaluations of their transferability to medical imaging, posing a challenge for practitioners in selecting the most proper pretrained models for their tasks at hand. To fill this gap, we conduct a comprehensive systematic study, focusing on (i) benchmarking numerous conventional and modern convolutional neural network (ConvNet) and vision transformer architectures across various medical tasks; (ii) investigating the impact of fine-tuning data size on the performance of ConvNets compared with vision transformers in medical imaging; (iii) examining the impact of pretraining data granularity on transfer learning performance; (iv) evaluating transferability of a wide range of recent self-supervised methods with diverse training objectives to a variety of medical tasks across different modalities; and (v) delving into the efficacy of domain-adaptive pretraining on both photographic and medical datasets to develop high-performance models for medical tasks. Our large-scale study ($\sim$5,000 experiments) yields impactful insights: (1) ConvNets demonstrate higher transferability than vision transformers when fine-tuning for medical tasks; (2) ConvNets prove to be more annotation efficient than vision transformers when fine-tuning for medical tasks; (3) Fine-grained representations, rather than high-level semantic features, prove pivotal for fine-grained medical tasks; (4) Self-supervised models excel in learning holistic features compared with supervised models; and (5) Domain-adaptive pretraining leads to performant models via harnessing knowledge acquired from ImageNet and enhancing it through the utilization of readily accessible expert annotations associated with medical datasets. As open science, all codes and pretrained models are available at GitHub.com/JLiangLab/BenchmarkTransferLearning (Version 2).