Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images
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| Publicado en: | NPJ Digital Medicine vol. 8, no. 1 (Dec 2025), p. 682-699 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
Nature Publishing Group
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| Resumen: | Artificial Intelligence has revolutionized healthcare by offering smart services and reducing diagnostic burden, particularly facilitating the identification and segmentation of malignant tissues. However, current task-specific approaches require disease-specific models, while universal foundation models demand costly customization for complex cases, hindering practical deployment in clinical environments. We present Pathology-NAS, a universal and lightweight medical analysis framework that leverages LLMs’ knowledge to refine the architecture space across diverse scenarios, eliminating the need for exhaustive search. Pathology-NAS is pretrained on 1.3 million images across three supernet architectures, providing a robust visual foundation that generalizes across diverse tasks. Across breast cancer and diabetic retinopathy diagnosis tasks, Pathology-NAS achieves 99.98% classification accuracy while reducing FLOPs by 45% compared to leading methods. Our model delivers near-optimal architectures in just 10 iterations, bypassing the exponential search space. Pathology-NAS provides accurate tumor recognition across diverse tissues with computational efficiency, making AI-assisted diagnosis practical even in resource-constrained clinical environments. |
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| ISSN: | 2398-6352 |
| DOI: | 10.1038/s41746-025-02042-x |
| Fuente: | Health & Medical Collection |