Weakly Supervised Pan-Cancer Segmentation Tool

Marvin Lerousseau, Marion Classe, Enzo Battistella, Théo Estienne, Théophraste Henry, Amaury Leroy, Roger Sun, Maria Vakalopoulou, Jean Yves Scoazec, Eric Deutsch, Nikos Paragios

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability. To address these issues, recent approaches have leveraged categorical annotations at the slide-level, that in general suffer from robustness and generalization. In this paper, we propose a novel weakly supervised multi-instance learning approach that deciphers quantitative slide-level annotations which are fast to obtain and regularly present in clinical routine. The extreme potentials of the proposed approach are demonstrated for tumor segmentation of solid cancer subtypes. The proposed approach achieves superior performance in out-of-distribution, out-of-location, and out-of-domain testing sets.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
    EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages248-256
    Number of pages9
    ISBN (Print)9783030872366
    DOIs
    Publication statusPublished - 1 Jan 2021
    Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
    Duration: 27 Sept 20211 Oct 2021

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12908 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
    CityVirtual, Online
    Period27/09/211/10/21

    Keywords

    • Weak supervision
    • Whole slide image tumor segmentation

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