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Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341724" target="_blank" >RIV/68407700:21230/20:00341724 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-030-50516-5_24" target="_blank" >https://doi.org/10.1007/978-3-030-50516-5_24</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-50516-5_24" target="_blank" >10.1007/978-3-030-50516-5_24</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Generalized Multiple Instance Learning for Cancer Detection in Digital Histopathology

  • Original language description

    We address the task of detecting cancer in histological slide images based on training with weak, slide- and patch-level annotations, which are considerably easier to obtain than pixel-level annotations. we use CNN based patch-level descriptors and formulate the image classification task as a generalized multiple instance learning (MIL) problem. The generalization consists of requiring a certain number of positive instances in positive bags, instead of just one as in standard MIL. The descriptors are learned on a small number of patch-level annotations, while the MIL layer uses only image-level patches for training. We evaluate multiple generalized MIL methods on the H&E stained images of lymphatic nodes from the CAMELYON dataset and show that generalized MIL methods improve the classification results and outperform no-MIL methods in terms of slide-level AUC. Best classification results were achieved by the MI-SVM(k) classifier in combination with simple spatial Gaussian aggregation, achieving AUC 0.962. However, MIL did not outperform methods trained on pixel-level segmentations.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Image Analysis and Recognition,17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24–26, 2020, Proceedings, Part II

  • ISBN

    978-3-030-50515-8

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    9

  • Pages from-to

    274-282

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Póvoa de Varzim

  • Event date

    Jun 24, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article