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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12008 |
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| _version_ | 1866912710570016768 |
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| author | Hong, Yunqi Kao, Johnson Edwards, Liam Liu, Nein-Tzu Huang, Chung-Yen Oliveira-Kowaleski, Alex Hsieh, Cho-Jui Lin, Neil Y. C. |
| author_facet | Hong, Yunqi Kao, Johnson Edwards, Liam Liu, Nein-Tzu Huang, Chung-Yen Oliveira-Kowaleski, Alex Hsieh, Cho-Jui Lin, Neil Y. C. |
| contents | AI tools in pathology have improved screening throughput, standardized quantification, and revealed prognostic patterns that inform treatment. However, adoption remains limited because most systems still lack the human-readable reasoning needed to audit decisions and prevent errors. We present RECAP-PATH, an interpretable framework that establishes a self-learning paradigm, shifting off-the-shelf multimodal large language models from passive pattern recognition to evidence-linked diagnostic reasoning. At its core is a two-phase learning process that autonomously derives diagnostic criteria: diversification expands pathology-style explanations, while optimization refines them for accuracy. This self-learning approach requires only small labeled sets and no white-box access or weight updates to generate cancer diagnoses. Evaluated on breast and prostate datasets, RECAP-PATH produced rationales aligned with expert assessment and delivered substantial gains in diagnostic accuracy over baselines. By uniting visual understanding with reasoning, RECAP-PATH provides clinically trustworthy AI and demonstrates a generalizable path toward evidence-linked interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models Hong, Yunqi Kao, Johnson Edwards, Liam Liu, Nein-Tzu Huang, Chung-Yen Oliveira-Kowaleski, Alex Hsieh, Cho-Jui Lin, Neil Y. C. Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning AI tools in pathology have improved screening throughput, standardized quantification, and revealed prognostic patterns that inform treatment. However, adoption remains limited because most systems still lack the human-readable reasoning needed to audit decisions and prevent errors. We present RECAP-PATH, an interpretable framework that establishes a self-learning paradigm, shifting off-the-shelf multimodal large language models from passive pattern recognition to evidence-linked diagnostic reasoning. At its core is a two-phase learning process that autonomously derives diagnostic criteria: diversification expands pathology-style explanations, while optimization refines them for accuracy. This self-learning approach requires only small labeled sets and no white-box access or weight updates to generate cancer diagnoses. Evaluated on breast and prostate datasets, RECAP-PATH produced rationales aligned with expert assessment and delivered substantial gains in diagnostic accuracy over baselines. By uniting visual understanding with reasoning, RECAP-PATH provides clinically trustworthy AI and demonstrates a generalizable path toward evidence-linked interpretation. |
| title | Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2511.12008 |