Reviews: Transfusion: Understanding Transfer Learning for Medical Imaging

The authors investigate the current transfer learning scheme for deep learning applications to medical imaging. They thoroughly assess and compare the performance of standard architectures that originally designed for the natural image classification tasks with their in-house-developed lightweight and simple models on medical imaging tasks. In this concern, the study demonstrates that latter models can perform comparably with computationally expensive state-of-the-art models. The second finding of the study is that transfer learning does not have a significant benefit for performance. The authors validate the claim by comparing the latent representations of the networks learned with the pretrained weights and training from scratch, and by measuring representational similarity with canonical correlation analysis (CCA).