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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

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  • Xiu Su,
  • Qinghua Mao,
  • Zhongze Wu,
  • Xi Lin,
  • Shan You,
  • Yue Liao,
  • Chang Xu

摘要

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.