MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis

2026-09-21

AI Summary

为解决自动模型对组织类型和图像采集差异敏感的问题，提出MiTHras框架，结合伪标签引导的对比学习与掩码重建方法，有效提升有丝分裂图分析性能。

Abstract

Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present MiTHras, a task-specific pretraining framework that combines pseudo-label-guided image- and token-level contrastive learning with masked reconstruction. We construct TCGA-MF-Pseudo, a corpus of 1.8 million cell-centered images from 14 TCGA cohorts spanning 11 organ sites. Comprehensive evaluation on MF classification, detection, count-based survival prediction, and subtype classification demonstrates the efficacy of MiTHras. It achieves the highest mean F1 on all three MF classification benchmarks and both subtype benchmarks. MiTHras also outperforms general-purpose and pathology foundation encoders by a larger margin under frozen-encoder linear probing than under full fine-tuning. Although detection gains are modest due to a shared candidate-detection stage, ablations confirm that token-level supervision improves typical-versus-atypical classification and linear probing. These findings establish that MiTHras yields robust, transferable representations for automated mitotic activity assessment.

Problem

• Mitotic Figure Analysis
• Tissue Type Sensitivity
• Image Acquisition Variability
• Automated Models

Innovation

• task-specific pretraining
• contrastive learning
• masked autoencoder
• pseudo-label-guided
• token-level supervision