Supervised Transfer Learning at Scale for Medical Imaging

by   Basil Mustafa, et al.

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.


page 2

page 5

page 6

page 7

page 16

page 17

page 19

page 20


A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis

Transfer learning from supervised ImageNet models has been frequently us...

MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks

Deep Learning (DL) requires a large amount of training data to provide q...

Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images

Transfer learning aims to exploit pre-trained models for more efficient ...

What Makes Transfer Learning Work For Medical Images: Feature Reuse Other Factors

Transfer learning is a standard technique to transfer knowledge from one...

Evaluating Knowledge Transfer In Neural Network for Medical Images

Deep learning and knowledge transfer techniques have permeated the field...

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging

With the increasingly varied applications of deep learning, transfer lea...

Training Deep Learning models with small datasets

The growing use of Machine Learning has produced significant advances in...