Rethink Transfer Learning in Medical Image Classification

Le Peng, Hengyue Liang, Taihui Li, Ju Sun

Transfer learning (TL) with deep convolutional neural networks (DCNNs) has proved successful in medical image classification (MIC). However, the current practice is puzzling, as MIC typically relies only on low- and/or mid-level features that are learned in the bottom layers of DCNNs. Following this intuition, we question the current strategies of TL in MIC. In this paper, we perform careful experimental comparisons between shallow and deep networks for classification on two chest x-ray datasets, using different TL strategies. We find that deep models are not always favorable, and finetuning truncated deep models almost always yields the best performance, especially in data-poor regimes. Project webpage: Keywords: Transfer learning, Medical image classification, Feature hierarchy, Medical imaging, Evaluation metrics, Imbalanced data

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