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 pre-training (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, pathology-dedicated CLI...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 422 - 439 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471456&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471456 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471456 184471456 184471456 10.1007/s10278-024-01128-4 184471456 ppf: 422 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Benchmarking PathCLIP for Pathology Image Analysis. aug: au: Zheng, Sunyi Cui, Xiaonan Sun, Yuxuan Li, Jingxiong Li, Honglin Zhang, Yunlong Chen, Pingyi Jing, Xueping Ye, Zhaoxiang Yang, Lin affil: https://ror.org/0152hn881 Tianjin'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 sug: subj: Osteosarcoma Diagnosis Lung Neoplasms Diagnosis Pathology, Clinical Image Processing, Computer Assisted Methods Benchmarking Predictive Value of Tests Evaluation Human Funding Source Databases Algorithms Decision Making Fraud Prevention and Control Data Quality Validation Studies Performance Measurement Systems Data Analysis, Statistical Models, Theoretical Image Retrieval Deep Learning Methods Instrument Construction ab: Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, pathology-dedicated CLIP (PathCLIP) has been developed, 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 eleven corruption types including brightness, contrast, defocus, resolution, saturation, hue, markup, deformation, incompleteness, rotation, and flipping at various settings. Through experiments, we find that PathCLIP surpasses OpenAI-CLIP and the pathology language-image pre-training (PLIP) model in zero-shot classification. It is relatively robust to image corruptions including contrast, saturation, incompleteness, and orientation factors. Among the eleven corruptions, hue, markup, deformation, defocus, and resolution can cause relatively severe performance fluctuation 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-to-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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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