MOOD 2020: A public benchmark for out-of-distribution detection and localization on medical images

Autor: David Zimmerer, Peter M. Full, Fabian Isensee, Paul Jager, Tim Adler, Jens Petersen, Gregor Kohler, Tobias Ross, Annika Reinke, Antanas Kascenas, Bjorn Sand Jensen, Alison Q. O'Neil, Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz, Nina Shvetsova, Irina Fedulova, Dmitry V. Dylov, Baolun Yu, Jianyang Zhai, Jingtao Hu, Runxuan Si, Sihang Zhou, Siqi Wang, Xinyang Li, Xuerun Chen, Yang Zhao, Sergio Naval Marimont, Giacomo Tarroni, Victor Saase, Lena Maier-Hein, Klaus Maier-Hein

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bd09f323a99e76832914cf9e01598d2
http://hdl.handle.net/10044/1/96881

A Multi-organ Nucleus Segmentation Challenge

Autor: Chunliang Wang, Chi-Hung Weng, Benjamin Chidester, Sihang Zhou, Gerald Schaefer, Debotosh Bhattacharjee, Xuhua Ren, Antanas Kascenas, Elad Arbel, Stefan Braunewell, Kailin Chen, Deepak Anand, Maria Gloria Bueno, Guanyu Cai, Peng Sun, Yanping Cui, Mostafa Jahanifar, Minh N. Do, Ali Gooya, Qian Wang, Linmin Pei, Minh-Triet Tran, Quoc Dang Vu, Valery Naranjo, Amir Ben-Dor, Adrián Colomer, Jin Tae Kwak, Alison O'Neil, Yanning Zhou, Ekaterina Sirazitdinova, Linlin Shen, Nasir M. Rajpoot, Baocai Yin, Ruchika Verma, Sabarinathan Devanathan, Dennis Eschweiler, Rupert Ecker, E. D. Tsougenis, Jian Ma, Raviteja Chunduri, Zihan Wu, Itay Remer, Kaushiki Roy, Amirreza Mahbod, Khan M. Iftekharuddin, Xinmei Tian, Neda Zamani Tajeddin, Isabella Ellinger, Corey Hu, Yuexiang Li, Jaegul Choo, Xiaojie Liu, Jun Ma, Dariush Lotfi, Erhardt Barth, Navid Alemi Koohbanani, Örjan Smedby, Simon Graham, Wei-Hsiang Yu, Omer Fahri Onder, Cheng-Kun Yang, Dinggang Shen, Yuqin Wang, Hao Chen, Pak-Hei Yeung, Xiaoyang Zhou, Reza Safdari, Pheng-Ann Heng, Shuang Yang, Zhiqiang Hu, Johannes Stegmaier, Amit Sethi, Akshaykumar Gunda, Chao-Yuan Yeh, Matthias Kohl, Jiahui Li, Shuoyu Xu, Mohammad Azam Khan, Xinpeng Xie, Praveen Koduganty, Neeraj Kumar, Philipp Gruening, Krishanu Das Baksi, Saravanan Radhakrishnan, That-Vinh Ton, Yunzhi Wang, Anibal Pedraza

Publikováno v: IEEE Transactions on Medical Imaging

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop ge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44290c831558b95a1b8370aab73f3546