{"slug": "tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and", "title": "TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation", "summary": "TAM-Chain, a multi-scale thyroid cytology classification framework combining Absorbing Markov Chain theory with Shannon Entropy-based uncertainty quantification, achieved a Macro F1 score of 0.9741 and an absolute False-Negative Rate of 0.00% on an internal test set of 235 cases, according to an arXiv paper (arXiv:2609.28590v1). On an independent external validation set of 1015 cases with severe domain shift, TAM-Chain maintained a Macro F1 of 0.7026 by adaptively adjusting its expected stopping step and triggering specialist referrals, outperforming single-magnification baselines. The framework models multi-magnification (10x, 20x, 40x) feature extraction as an absorbing stochastic process to support optimal stopping and human-in-the-loop referral for false-negative suppression.", "body_md": "arXiv:2609.28590v1 Announce Type: new \nAbstract: 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.\n  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.\n  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.\n  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.", "url": "https://wpnews.pro/news/tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and", "canonical_source": "https://arxiv.org/abs/2609.28590", "published_at": "2026-09-25 04:00:00+00:00", "updated_at": "2026-09-25 04:30:09.877127+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "ai-research"], "entities": ["TAM-Chain", "arXiv", "Bethesda System"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and", "markdown": "https://wpnews.pro/news/tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and.md", "text": "https://wpnews.pro/news/tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and.txt", "jsonld": "https://wpnews.pro/news/tam-chain-multi-scale-thyroid-cytology-classification-via-absorbing-markov-and.jsonld"}}