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September 24, 2025Open Access

A multi-task neural network for atypical mitosis recognition under domain shift

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This approach uses multi-task learning to improve atypical mitosis recognition, addressing domain shift challenges.

The proposed method improves atypical mitosis classification under domain shift, enhancing tumor analysis.
Preliminary results show promising performance across three datasets, including MIDOG 2025 Atypical Training Set.
The multi-task learning framework helps focus classification on relevant features, minimizing background distraction.
Implications suggest potential for robust tumor assessment using machine learning in variable image conditions.

Percannella et al. (2025) studied this question.

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