---
id: 20260927-T0-04
title: "TAM-Chain用吸收马尔可夫链降低甲状腺细胞学假阴性，并适应域偏移"
title_en: "TAM-Chain Cuts Thyroid Cytology False Negatives via Absorbing Markov Chains"
url: https://ai.daily.yangsir.net/daily/20260927-T0-04
issue_date: 2026-09-27
publish_date: 2026-09-26T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.28590
---

# TAM-Chain用吸收马尔可夫链降低甲状腺细胞学假阴性，并适应域偏移

甲状腺细针穿刺活检（FNAB）细胞学基于Bethesda系统，是早期甲状腺癌检测的关键手段，但深度学习模型面临高假阴性和域偏移两大难题。该研究提出TAM-Chain框架，用吸收马尔可夫链做多尺度分类，并以香农熵量化不确定性，从而抑制假阴性并适应不同来源的数据分布。核心效果是：在保持分类精度的同时，降低漏诊率，并提升跨域泛化能力。对临床AI开发者而言，这意味着细胞学辅助诊断系统可以在不同医院的数据上更稳定地运行，减少因设备或染色差异导致的误判。

## English Version

**TAM-Chain Cuts Thyroid Cytology False Negatives via Absorbing Markov Chains**

Thyroid fine-needle aspiration biopsy (FNAB) cytology based on the Bethesda System is critical for early thyroid cancer detection, but deep learning faces high false-negative rates and domain shift. This paper proposes TAM-Chain, a framework using absorbing Markov chains for multi-scale classification and Shannon entropy for uncertainty quantification, suppressing false negatives and adapting to domain shift. The key effect: lower miss rates while maintaining classification accuracy, plus improved cross-domain generalization. For clinical AI developers, this means cytology-aided diagnostic systems can run more stably across data from different hospitals, reducing misjudgments caused by equipment or staining variations.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.28590)

**详情页**：https://ai.daily.yangsir.net/daily/20260927-T0-04

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