TY - JOUR
T1 - LYSTO
T2 - The Lymphocyte Assessment Hackathon and Benchmark Dataset
AU - Jiao, Yiping
AU - Van Der Laak, Jeroen
AU - Albarqouni, Shadi
AU - Li, Zhang
AU - Tan, Tao
AU - Bhalerao, Abhir
AU - Cheng, Shenghua
AU - Ma, Jiabo
AU - Pocock, Johnathan
AU - Pluim, Josien P.W.
AU - Koohbanani, Navid Alemi
AU - Bashir, Raja Muhammad Saad
AU - Raza, Shan E.Ahmed
AU - Liu, Sibo
AU - Graham, Simon
AU - Wetstein, Suzanne
AU - Khurram, Syed Ali
AU - Liu, Xiuli
AU - Rajpoot, Nasir
AU - Veta, Mitko
AU - Ciompi, Francesco
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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.
AB - 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.
KW - Lymphocyte assessment
KW - artificial intelligence
KW - computational pathology
KW - computer-aided diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85176307443&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2023.3327489
DO - 10.1109/JBHI.2023.3327489
M3 - Article
C2 - 37878422
AN - SCOPUS:85176307443
SN - 2168-2194
VL - 28
SP - 1161
EP - 1172
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 3
ER -