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Benchmarking PathCLIP for Pathology Image Analysis.

Sunyi Zheng1,2, Xiaonan Cui1, Yuxuan Sun3

  • 1Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.

Journal of Imaging Informatics in Medicine
|July 9, 2024
PubMed
Summary

Pathology-dedicated CLIP (PathCLIP) shows strong zero-shot classification but varies in robustness to image corruptions. Careful image quality assessment is crucial for reliable clinical AI applications.

Keywords:
Deep learningFoundation modelImage retrievalPathology image analysisZero-shot classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Accurate image classification and retrieval are vital for clinical diagnosis and treatment decisions.
  • Contrastive Language-Image Pre-training (CLIP) models excel in natural image understanding.
  • Pathology-dedicated CLIP (PathCLIP) leverages extensive pathology image-text data.

Purpose of the Study:

  • To evaluate the robustness of PathCLIP against various image corruptions.
  • To compare PathCLIP's performance with OpenAI-CLIP and PLIP under corrupted conditions.
  • To assess PathCLIP's reliability in zero-shot classification and image retrieval tasks.

Main Methods:

  • PathCLIP was tested on osteosarcoma and WSSS4LUAD datasets with eleven corruption types (e.g., brightness, contrast, hue, deformation).
  • Performance was analyzed for zero-shot classification and image-to-image retrieval.
  • Comparative analysis against OpenAI-CLIP and Pathology Language-Image Pre-training (PLIP) was conducted.

Main Results:

  • PathCLIP outperformed OpenAI-CLIP and PLIP in zero-shot classification.
  • PathCLIP demonstrated relative robustness to contrast, saturation, incompleteness, and orientation corruptions.
  • Hue, markup, deformation, defocus, and resolution corruptions significantly impacted PathCLIP performance.
  • Retrieval performance varied, with PathCLIP underperforming PLIP on osteosarcoma but outperforming it on WSSS4LUAD.

Conclusions:

  • PathCLIP exhibits strong potential for pathology image analysis but requires careful consideration of image quality.
  • Image quality assurance is critical for the reliable deployment of AI in clinical settings.
  • Further research into PathCLIP's resilience to specific corruptions is warranted.