How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?

Xingyu Li, Konstantinos N. Plataniotis

Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aiming to build effective pathology image diagnosis models. Since transferability of knowledge heavily depends on the similarity of the original and target tasks, significant differences in image content and statistics between pathology images and natural images raise the questions: how much knowledge is transferable? Is the transferred information equally contributed by pre-trained layers? To answer these questions, this paper proposes a method to quantify knowledge gain by a particular layer, conducts an empirical investigation into pathology image centered transfer learning, and reports some interesting observations. Different from prior arts in transfer learning, we show that transferability of off-the-shelf representation heavily depends on specific pathology image sets, and may obtain classification performance close to random guess. The observation in this study encourages further investigation of specific metric and tools to quantify the feasibility of transfer learning in future.

Knowledge Graph

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