Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images.

ABSTRACT: 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 breas

SUBMITTER: Su X 

PROVIDER: S-EPMC12627663 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature