Semi-supervised Pathology Segmentation with Disentangled Representations

Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Marc Dweck, David Semple, Rohan Dharmakumar, Sotirios Tsaftaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semisupervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation model that attempts to learn jointly for the first time: disentanglement of anatomy, modality, and pathology. The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations. In addition, a joint optimization strategy is proposed to fully take advantage of the available annotations. We evaluate our methods with two private cardiac infarction segmentation datasets with LGE-MRI scans. APD-Net can perform pathology segmentation with few annotations, maintain performance with different amounts of supervision, and outperform related deep learning methods.
Original languageEnglish
Title of host publication DART: MICCAI Workshop on Domain Adaptation and Representation Transfer
Subtitle of host publicationDomain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
PublisherSpringer
Pages62-72
ISBN (Electronic)978-3-030-60548-3
ISBN (Print)978-3-030-60547-6
DOIs
Publication statusE-pub ahead of print - 26 Sep 2020
EventMICCAI 2020 : DART Workshop -
Duration: 8 Sep 20208 Sep 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Link
Volume12444
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMICCAI 2020
Period8/09/208/09/20

Keywords

  • pathology segmentation
  • disentangled representations
  • semi- supervised learning

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