# Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification

## TL;DR

This paper introduces a robust domain-adaptive method for classifying atypical mitotic figures in histopathology images, combining style perturbations, feature alignment, and knowledge distillation to handle domain shifts effectively.

## Contribution

It proposes a novel training recipe that enhances domain robustness in mitosis classification using style perturbations, attention-based feature alignment, and EMA teacher distillation.

## Key findings

- Achieved balanced accuracy of 0.8762 on leaderboard
- Attained sensitivity of 0.8873 and specificity of 0.8651
- Reached ROC AUC of 0.9499 with negligible inference overhead

## Abstract

Atypical mitotic figures (AMFs) are important histopathological markers yet remain challenging to identify consistently, particularly under domain shift stemming from scanner, stain, and acquisition differences. We present a simple training-time recipe for domain-robust AMF classification in MIDOG 2025 Task 2. The approach (i) increases feature diversity via style perturbations inserted at early and mid backbone stages, (ii) aligns attention-refined features across sites using weak domain labels (Scanner, Origin, Species, Tumor) through an auxiliary alignment loss, and (iii) stabilizes predictions by distilling from an exponential moving average (EMA) teacher with temperature-scaled KL divergence. On the organizer-run preliminary leaderboard for atypical mitosis classification, our submission attains balanced accuracy of 0.8762, sensitivity of 0.8873, specificity of 0.8651, and ROC AUC of 0.9499. The method incurs negligible inference-time overhead, relies only on coarse domain metadata, and delivers strong, balanced performance, positioning it as a competitive submission for the MIDOG 2025 challenge.