6 papers · 1 filter
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
Yuxuan Sun, Yixuan Si, Chenglu Zhu +7
The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and wh…
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Honglin Li, Yunlong Zhang, Pingyi Chen +3
Histopathology Whole Slide Image (WSI) analysis serves as the gold standard for clinical cancer diagnosis in the daily routines of doctors. To develop computer-aided diagnosis mode…
Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis
Honglin Li, Yusuan Sun, Chenglu Zhu +8
Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image…
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Pingyi Chen, Chenglu Zhu, Sunyi Zheng +2
Whole slide imaging is routinely adopted for carcinoma diagnosis and prognosis. Abundant experience is required for pathologists to achieve accurate and reliable diagnostic results…
WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
Pingyi Chen, Honglin Li, Chenglu Zhu +3
Whole slide images are the foundation of digital pathology for the diagnosis and treatment of carcinomas. Writing pathology reports is laborious and error-prone for inexperienced p…
Benchmarking PathCLIP for Pathology Image Analysis
Sunyi Zheng, Xiaonan Cui, Yuxuan Sun +7
Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pretraining (CLIP) model h…