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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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