A Closer Look at Domain Shift for Deep Learning in Histopathology

Abstract

Domain shift is a significant problem in histopathology. There can be large differences in data characteristics of whole-slide images between medical centers and scanners, making generalization of deep learning to unseen data difficult. To gain a better understanding of the problem, we present a study on convolutional neural networks trained for tumor classification of H&E stained whole-slide images. We analyze how augmentation and normalization strategies affect performance and learned representations, and what features a trained model respond to. Most centrally, we present a novel measure for evaluating the distance between domains in the context of the learned representation of a particular model. This measure can reveal how sensitive a model is to domain variations, and can be used to detect new data that a model will have problems generalizing to. The results show how learning is heavily influenced by the preparation of training data, and that the latent representation used to do classification is sensitive to changes in data distribution, especially when training without augmentation or normalization.

arXiv comment8 pages, 4 figures. Accepted to COMPAY2019: Second MICCAI Workshop on Computational Pathology

References in corpus (4)

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Transfusion: Understanding Transfer Learning for Medical Imaging
• Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

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• A benchmark with decomposed distribution shifts for 360 monocular depth estimation
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• An Empirical Framework for Domain Generalization in Clinical Settings