Benchmarking PathCLIP for Pathology Image Analysis
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingDecision Makingimage-classificationImage ClassificationImage RetrievalRetrievalzero-shot-classificationZero-Shot LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, offering significant applications in inter…
DiagnosticInstruction FollowingLanguage Modellingmultimodal generationBenchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios
In computational pathology, several foundation models have recently emerged and demonstrated enhanced learning capability for analyzing pathology images. However, adapting these models to various downstream tasks remains…
BenchmarkingFew-Shot Learningparameter-efficient fine-tuningUnPuzzle: A Unified Framework for Pathology Image Analysis
Pathology image analysis plays a pivotal role in medical diagnosis, with deep learning techniques significantly advancing diagnostic accuracy and research. While numerous studies have been conducted to address specific p…
BenchmarkingDiagnosticMedical DiagnosisMulti-Task Learning+1Evaluating histopathology transfer learning with ChampKit
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, includi…
BenchmarkingCell DetectionGeneral Classificationimage-classification+3A Survey of Pathology Foundation Model: Progress and Future Directions
Computational pathology, which involves analyzing whole slide images for automated cancer diagnosis, relies on multiple instance learning, where performance depends heavily on the feature extractor and aggregator. Recent…
BenchmarkingMultiple Instance LearningPhilosophySurvey+1