Paper Info
Title
From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification
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.
Year
DOI
Venue
2019
10.1007/978-3-030-32239-7_40
Lecture Notes in Computer Science
Keywords
DocType
Volume
Histopathology image classification,Unsupervised domain adaptation,Deep learning,Microscopy image,While slide image
Conference
11764
ISSN
Citations 
PageRank 
0302-9743
6
0.44
References 
Authors
0
14
Name
Order
Citations
PageRank
Yifan Zhang1102.90
Hanbo Chen271.14
Ying Wei31039.51
Peilin Zhao4136580.09
Jiezhang Cao5164.30
Xinjuan Fan681.15
Xiaoying Lou770.80
Hailing Liu8175.09
Jinlong Hou960.44
Xiao Han1081675.26
Jianhua Yao111135110.49
Wu Qingyao1225933.46
Rui Tang1318819.22
Junzhou Huang142182141.43