TAM-Chain：基于吸收马尔可夫链与香农熵不确定性量化的多尺度甲状腺细胞学分类，用于假阴性抑制与域偏移适应
TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
- Faculty of Mathematics, Mechanics and Informatics, VNU University of Science（越南国立大学理学院数学、力学与信息学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对甲状腺FNAB细胞学分类中假阴性率高和域偏移下的过度自信问题，提出基于吸收马尔可夫链和香农熵不确定性量化的多尺度框架TAM-Chain，通过最优停止与人机协同转诊，实现高准确率与零假阴性。
AI中文摘要：
背景与问题：基于Bethesda系统的甲状腺细针穿刺活检（FNAB）细胞学在早期甲状腺癌检测中发挥着关键作用；然而，深度学习方法在临床域偏移下面临高假阴性率和过度自信的重大挑战。方法：在本研究中，我们提出了TAM-Chain，一种多尺度（10x、20x、40x）甲状腺细胞学分类框架，利用吸收马尔可夫链理论结合基于香农熵的不确定性量化。该框架将多倍率特征提取动态建模为吸收随机过程，从而实现最优停止准则和人机协同转诊机制，以严格抑制关键诊断错误。结果：在内部测试集（N=235）上的广泛评估显示，Macro F1得分为0.9741，绝对假阴性率（FNR）为0.00%。在呈现严重域偏移的独立外部验证集（N=1015）上，TAM-Chain通过自适应调整预期停止步数并触发专家转诊，保持了优异的稳定性和分类性能（Macro F1=0.7026），显著优于单倍率基线。结论：TAM-Chain框架被证明是数字病理工作流程中高效、安全且适应性强的解决方案，成功协调了自动化诊断效率与严格的生物安全性。
英文摘要：
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.
