A multi-task neural network for atypical mitosis recognition under domain shift
This work addresses domain generalization for tumor aggressiveness assessment in histopathology, but it appears incremental as it builds on existing multi-task learning methods.
The paper tackles the problem of recognizing atypical mitotic figures in histopathology images under domain shift, proposing a multi-task learning approach that shows promising performance in preliminary evaluations on three datasets.
Recognizing atypical mitotic figures in histopathology images allows physicians to correctly assess tumor aggressiveness. Although machine learning models could be exploited for automatically performing such a task, under domain shift these models suffer from significative performance drops. In this work, an approach based on multi-task learning is proposed for addressing this problem. By exploiting auxiliary tasks, correlated to the main classification task, the proposed approach, submitted to the track 2 of the MItosis DOmain Generalization (MIDOG) challenge, aims to aid the model to focus only on the object to classify, ignoring the domain varying background of the image. The proposed approach shows promising performance in a preliminary evaluation conducted on three distinct datasets, i.e., the MIDOG 2025 Atypical Training Set, the Ami-Br dataset, as well as the preliminary test set of the MIDOG25 challenge.