A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis

• URL: http://arxiv.org/abs/2108.05930v1
• Date: Thu, 12 Aug 2021 19:08:34 GMT
• Title: A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
• Authors: Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng, Michael B. Gotway, Jianming Liang
• Abstract summary: We conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale fine-grained dataset. We present a practical approach to bridge the domain gap between natural and medical images by continually (pre-training) supervised ImageNet models on medical images.
• Score: 7.339428207644444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale fine-grained dataset, and 14 top self-supervised ImageNet models on 7 diverse medical tasks in comparison with the supervised ImageNet model. Furthermore, we present a practical approach to bridge the domain gap between natural and medical images by continually (pre-)training supervised ImageNet models on medical images. Our comprehensive evaluation yields new insights: (1) pre-trained models on fine-grained data yield distinctive local representations that are more suitable for medical segmentation tasks, (2) self-supervised ImageNet models learn holistic features more effectively than supervised ImageNet models, and (3) continual pre-training can bridge the domain gap between natural and medical images. We hope that this large-scale open evaluation of transfer learning can direct the future research of deep learning for medical imaging. As open science, all codes and pre-trained models are available on our GitHub page https://github.com/JLiangLab/BenchmarkTransferLearning.

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