Supervised Transfer Learning at Scale for Medical Imaging

• URL: http://arxiv.org/abs/2101.05913v3
• Date: Thu, 21 Jan 2021 18:07:21 GMT
• Title: Supervised Transfer Learning at Scale for Medical Imaging
• Authors: Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott Mayer McKinney, Marcin Sieniek, Jim Winkens, Yuan Liu, Peggy Bui, Shruthi Prabhakara, Umesh Telang, Alan Karthikesalingam, Neil Houlsby and Vivek Natarajan
• Abstract summary: We investigate whether modern methods can change the fortune of transfer learning for medical imaging. We study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three diverse imaging tasks. We find that for some of these properties transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.
• Score: 8.341246672632582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three diverse imaging tasks: chest radiography, mammography, and dermatology. We study both transfer performance and critical properties for the deployment in the medical domain, including: out-of-distribution generalization, data-efficiency, sub-group fairness, and uncertainty estimation. Interestingly, we find that for some of these properties transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.

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