TAM-Chain: 基于吸收马尔可夫链和香农熵不确定性量化的多尺度甲状腺细胞学分类方法，用于假阴性抑制和域偏移适应

arXiv cs.LG 论文
thyroid-cytology classification markov-chains entropy uncertainty-quantification medical-imaging domain-adaptation

摘要

本文介绍了TAM-Chain，一种采用吸收马尔可夫链和香农熵进行不确定性量化的多尺度甲状腺细胞学分类方法，旨在抑制假阴性并适应域偏移。

arXiv:2609.28590v1 Announce Type: new Abstract: Background & Problem: Thyroid Fine-Needle Aspiration Biopsy (FNAB) cytology based on the Bethesda System plays a pivotal role in early thyroid cancer detection; however, deep learning approaches face substantial challenges regarding high false-negative rates and overconfidence under clinical domain shift. Methods: In this study, we propose TAM-Chain, a multi-scale (10x, 20x, 40x) thyroid cytology classification framework leveraging Absorbing Markov Chain theory combined with Shannon Entropy-based Uncertainty Quantification. The framework dynamically models multi-magnification feature extraction as an absorbing stochastic process, enabling optimal stopping criteria and a human-in-the-loop referral mechanism to strictly suppress critical diagnostic errors. Results: Extensive evaluation on an internal test set (N = 235) demonstrates a Macro F1 score of 0.9741 with an absolute False-Negative Rate (FNR) of 0.00%. On an independent external validation set (N = 1015) presenting severe domain shift, TAM-Chain maintains superior stability and classification performance (Macro F1 = 0.7026) by adaptively adjusting the expected stopping step and triggering specialist referrals, significantly outperforming single-magnification baselines. Conclusion: The TAM-Chain framework proves to be a highly effective, safe, and adaptable solution for digital pathology workflows, successfully harmonizing automated diagnostic efficiency with stringent biological safety.
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# TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
Source: [https://arxiv.org/abs/2609.28590](https://arxiv.org/abs/2609.28590)
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