Source-free domain adaptation of weakly-supervised object localization models for histology

Alexis Guichemerre*, Soufiane Belharbi, Tsiry Mayet, Shakeeb Murtaza, Pourya Shamsolmoali, Luke McCaffrey, Eric Granger*

*Corresponding author for this work

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

Abstract

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Came-lyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification. Code: github.com/AlexisGuichemerreCode/survey_hist_wsol_sfda

Original languageEnglish
Title of host publication2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages33-43
Number of pages11
ISBN (Electronic)9798350365474
ISBN (Print)9798350365481
DOIs
Publication statusPublished - 27 Sept 2024
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 - Seattle, United States
Duration: 17 Jun 202421 Jun 2024
Conference number: 37

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Proceedings
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Abbreviated titleCVPR 2024
Country/TerritoryUnited States
CitySeattle
Period17/06/202421/06/2024

Keywords

  • source-free domain adaptation
  • weakly-supervised object localization models
  • histology

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