CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

• URL: http://arxiv.org/abs/2412.12077v1
• Date: Mon, 16 Dec 2024 18:46:58 GMT
• Title: CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
• Authors: Yuxuan Sun, Yixuan Si, Chenglu Zhu, Xuan Gong, Kai Zhang, Pingyi Chen, Ye Zhang, Zhongyi Shui, Tao Lin, Lin Yang,
• Abstract summary: CPath- Omni is the first LMM designed to unify both patch and WSI level image analysis.<n>CPath- Omni achieves state-of-the-art (SOTA) performance across seven diverse tasks on 39 out of 42 datasets.<n>CPath-CLIP, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model.
• Score: 17.781388341968967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learned knowledge across patches and WSIs, and resulting in redundant models. In this work, we introduce CPath-Omni, the first 15-billion-parameter LMM designed to unify both patch and WSI level image analysis, consolidating a variety of tasks at both levels, including classification, visual question answering, captioning, and visual referring prompting. Extensive experiments demonstrate that CPath-Omni achieves state-of-the-art (SOTA) performance across seven diverse tasks on 39 out of 42 datasets, outperforming or matching task-specific models trained for individual tasks. Additionally, we develop a specialized pathology CLIP-based visual processor for CPath-Omni, CPath-CLIP, which, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model, which achieves SOTA performance on nine zero-shot and four few-shot datasets. Our findings highlight CPath-Omni's ability to unify diverse pathology tasks, demonstrating its potential to streamline and advance the field of foundation model in pathology.

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