@InProceedings{10.1007/978-3-030-32239-7_40,
author="Zhang, Yifan
and Chen, Hanbo
and Wei, Ying
and Zhao, Peilin
and Cao, Jiezhang
and Fan, Xinjuan
and Lou, Xiaoying
and Liu, Hailing
and Hou, Jinlong
and Han, Xiao
and Yao, Jianhua
and Wu, Qingyao
and Tan, Mingkui
and Huang, Junzhou",
editor="Shen, Dinggang
and Liu, Tianming
and Peters, Terry M.
and Staib, Lawrence H.
and Essert, Caroline
and Zhou, Sean
and Yap, Pew-Thian
and Khan, Ali",
title="From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="360--368",
abstract="Deep learning (DL) has achieved remarkable performance on digital pathology image classification with whole slide images (WSIs). Unfortunately, high acquisition costs of WSIs hinder the applications in practical scenarios, and most pathologists still use microscopy images (MSIs) in their workflows. However, it is especially challenging to train DL models on MSIs, given limited image qualities and high annotation costs. Alternatively, directly applying a WSI-trained DL model on MSIs usually performs poorly due to huge gaps between WSIs and MSIs. To address these issues, we propose to exploit deep unsupervised domain adaptation to adapt DL models trained on the labeled WSI domain to the unlabeled MSI domain. Specifically, we propose a novel Deep Microscopy Adaptation Network (DMAN). By reducing domain discrepancies via adversarial learning and entropy minimization, and alleviating class imbalance with sample reweighting, DMAN can classify MSIs effectively even without MSI annotations. Extensive experiments on colon cancer diagnosis demonstrate the effectiveness of DMAN and its potential in customizing models for each pathologist's microscope.",
isbn="978-3-030-32239-7"
}