Paper Info
Title
Data-Efficient Histopathology Image Analysis with Deformation Representation Learning.
Abstract
Histopathological examination of tissue biopsies plays a fundamental role in disease assessment. Automatic histopathology image analysis requires substantial task-specific annotations, which are often expensive and laborious in realworld scenarios. This insufficient annotation of data limits the generalization ability of supervised learning models. To address this challenge, we propose a self-supervised Deformation Representation Learning (DRL) framework to learn semantic features from unlabeled data. As a novel paradigm, our approach utilizes deformation as supervisory signals based on two critical features, i.e., local structure heterogeneity and global context homogeneity. Given an original histopathology image and its deformed counterpart, there exists a moderate difference in local structures. In contrast, due to the transformation-invariance, both images share a similar global context compared with other images. Specifically, an encoder network is trained to distinguish the local inconsistency by measuring the mutual information and maintain the global consistency with noise contrastive estimation. Extensive experiments on public histopathology image datasets show that the learned representations are generalizable for various downstream tasks, such as transfer learning on segmentation and semi-supervised classification. Our approach achieves superior results over other self-supervised methods and the ImageNet pre-trained model, and it reveals the ability as a novel pre-training scheme in histopathology image analysis.
Year
DOI
Venue
2020
10.1109/BIBM49941.2020.9313159
BIBM
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Jilan Xu111.74
Junlin Hou212.41
Yuejie Zhang300.34
Rui Feng48914.12
Chunyang Ruan542.42
Tao Zhang641.06
Weiguo Fan72055133.38