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Jiao, Y.* ; van der Laak, J.* ; Albarqouni, S. ; Li, Z.* ; Tan, T.* ; Bhalerao, A.* ; Cheng, S.* ; Ma, J.* ; Pocock, J.M.* ; Pluim, J.P.W.* ; Koohbanani, N.A.* ; Bashir, R.M.S.* ; Raza, S.E.A.* ; Liu, S.* ; Graham, S.E.* ; Wetstein, S.* ; Khurram, S.A.* ; Liu, X.* ; Rajpoot, N.* ; Veta, M.* ; Ciompi, F.*

LYSTO: The lymphocyte assessment hackathon and benchmark dataset.

IEEE J. Biomed. Health Inform. 28, 1161-1172 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform. LYSTO has supported a number of research in lymphocyte assessment in oncology. LYSTO will be a long-lasting educational challenge for deep learning and digital pathology, it is available at https://lysto.grand-challenge.org/.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Artificial Intelligence ; Computational Pathology ; Computer-aided Diagnosis ; Lymphocyte Assessment; Immune Contexture; Breast-cancer; Quantification; Segmentation; Images
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2168-2194
e-ISSN 2168-2208
Zeitschrift IEEE Journal of Biomedical and Health Informatics
Quellenangaben Band: 28, Heft: 3, Seiten: 1161-1172 Artikelnummer: , Supplement: ,
Verlag IEEE
Verlagsort 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530005-001
Förderungen European Union#x0027
s Horizon 2020 Research and Innovation Programme
DOI 10.1109/JBHI.2023.3327489
WOS ID 001180907300050
Scopus ID 85176307443
PubMed ID 37878422
Erfassungsdatum 2023-12-15
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