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Benchmarking PathCLIP for Pathology Image Analysis

Sunyi Zheng, Xiaonan Cui, Yuxuan Sun, Jingxiong Li, Honglin Li, Yunlong Zhang, Pingyi Chen, Xueping Jing, Zhaoxiang Ye, Lin Yang

TL;DR

The paper systematically benchmarks PathCLIP's robustness for pathology image analysis under seven corruptions across four severity levels, comparing zero-shot classification and image-image retrieval to OpenAI-CLIP and PLIP on Osteosarcoma and WSSS4LUAD datasets. It combines a pathology-focused CLIP framework with a diverse corruption suite and standard metrics to reveal task-dependent strengths: PathCLIP excels in zero-shot classification, while PLIP often leads in retrieval, with blur and resolution posing the greatest robustness challenges. The findings support using PathCLIP as a strong zero-shot classifier in pathology, but emphasize image quality control and model selection based on the specific clinical task. The work provides practical guidance for deploying foundation models in pathology and highlights avenues for enhancing robustness via corrupted-data training and multimodal integration with language models.

Abstract

Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pretraining (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, PathCLIP is specifically designed for pathology image analysis, utilizing over 200,000 image and text pairs in training. While the performance the PathCLIP is impressive, its robustness under a wide range of image corruptions remains unknown. Therefore, we conduct an extensive evaluation to analyze the performance of PathCLIP on various corrupted images from the datasets of Osteosarcoma and WSSS4LUAD. In our experiments, we introduce seven corruption types including brightness, contrast, Gaussian blur, resolution, saturation, hue, and markup at four severity levels. Through experiments, we find that PathCLIP is relatively robustness to image corruptions and surpasses OpenAI-CLIP and PLIP in zero-shot classification. Among the seven corruptions, blur and resolution can cause server performance degradation of the PathCLIP. This indicates that ensuring the quality of images is crucial before conducting a clinical test. Additionally, we assess the robustness of PathCLIP in the task of image-image retrieval, revealing that PathCLIP performs less effectively than PLIP on Osteosarcoma but performs better on WSSS4LUAD under diverse corruptions. Overall, PathCLIP presents impressive zero-shot classification and retrieval performance for pathology images, but appropriate care needs to be taken when using it. We hope this study provides a qualitative impression of PathCLIP and helps understand its differences from other CLIP models.

Benchmarking PathCLIP for Pathology Image Analysis

TL;DR

The paper systematically benchmarks PathCLIP's robustness for pathology image analysis under seven corruptions across four severity levels, comparing zero-shot classification and image-image retrieval to OpenAI-CLIP and PLIP on Osteosarcoma and WSSS4LUAD datasets. It combines a pathology-focused CLIP framework with a diverse corruption suite and standard metrics to reveal task-dependent strengths: PathCLIP excels in zero-shot classification, while PLIP often leads in retrieval, with blur and resolution posing the greatest robustness challenges. The findings support using PathCLIP as a strong zero-shot classifier in pathology, but emphasize image quality control and model selection based on the specific clinical task. The work provides practical guidance for deploying foundation models in pathology and highlights avenues for enhancing robustness via corrupted-data training and multimodal integration with language models.

Abstract

Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pretraining (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, PathCLIP is specifically designed for pathology image analysis, utilizing over 200,000 image and text pairs in training. While the performance the PathCLIP is impressive, its robustness under a wide range of image corruptions remains unknown. Therefore, we conduct an extensive evaluation to analyze the performance of PathCLIP on various corrupted images from the datasets of Osteosarcoma and WSSS4LUAD. In our experiments, we introduce seven corruption types including brightness, contrast, Gaussian blur, resolution, saturation, hue, and markup at four severity levels. Through experiments, we find that PathCLIP is relatively robustness to image corruptions and surpasses OpenAI-CLIP and PLIP in zero-shot classification. Among the seven corruptions, blur and resolution can cause server performance degradation of the PathCLIP. This indicates that ensuring the quality of images is crucial before conducting a clinical test. Additionally, we assess the robustness of PathCLIP in the task of image-image retrieval, revealing that PathCLIP performs less effectively than PLIP on Osteosarcoma but performs better on WSSS4LUAD under diverse corruptions. Overall, PathCLIP presents impressive zero-shot classification and retrieval performance for pathology images, but appropriate care needs to be taken when using it. We hope this study provides a qualitative impression of PathCLIP and helps understand its differences from other CLIP models.
Paper Structure (15 sections, 4 equations, 5 figures, 2 tables)

This paper contains 15 sections, 4 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Robustness assessment of CLIP on natural images
  4. Corruption analysis on pathology images
  5. Methods
  6. Datasets
  7. Image Corruptions
  8. CLIP framework
  9. Evaluation metric
  10. Implementation details
  11. Experiments and results
  12. Performance on zero-shot image classification
  13. Performance on image-image retrieval
  14. Model comparisons
  15. Conclusion

Figures (5)

  • Figure 1: Examples of corrupted images. (a)-(h) represent the corruption of brightness, contrast, Gaussian blur, resolution, saturation, hue, and markup, respectively.
  • Figure 2: Performance of PathCLIP on image corruptions varies across four severity levels, ranging from mild to severe, denoted as S-A to S-D. The bold values represent the maximum prediction probabilities, while the underlined values indicate the ground truth classes.
  • Figure 3: Example results of PathCLIP on image corruptions with the correct class name displayed below each image.
  • Figure 4: Model comparisons on zero-shot classification.
  • Figure 5: Model comparisons on image-image retrieval.