Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies

no code implementations • 2 Apr 2024 • Hongyan Gu, Zihan Yan, Ayesha Alvi, Brandon Day, Chunxu Yang, Zida Wu, Shino Magaki, Mohammad Haeri, Xiang 'Anthony' Chen

We assessed the efficacy of such eye-gaze labels by training Convolutional Neural Networks (CNNs) and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline.

Mitosis Detection

Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge

no code implementations • 27 Sep 2023 • Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan, Robert Klopfleisch, Jonathan Ganz, Jonas Ammeling, Frauke Wilm, Mitko Veta, Samir Jabari, Markus Eckstein, Jonas Annuscheit, Christian Krumnow, Engin Bozaba, Sercan Cayir, Hongyan Gu, Xiang 'Anthony' Chen, Mostafa Jahanifar, Adam Shephard, Satoshi Kondo, Satoshi Kasai, Sujatha Kotte, VG Saipradeep, Maxime W. Lafarge, Viktor H. Koelzer, Ziyue Wang, Yongbing Zhang, Sen yang, Xiyue Wang, Katharina Breininger, Christof A. Bertram

The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains.

Domain Generalization

Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

no code implementations • 26 Aug 2022 • Hongyan Gu, Mohammad Haeri, Shuo Ni, Christopher Kazu Williams, Neda Zarrin-Khameh, Shino Magaki, Xiang 'Anthony' Chen

This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN).

Active Learning Data Augmentation +1