Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation

作者：Shahad Albastaki, A. Sohail, I. I. Ganapathi, B. Alawode, Asim Khan, Sajid Javed, N. Werghi, Mohammed Bennamoun, Arif Mahmood · 发表于：Computer Vision and Pattern Recognition · 年份：2025 · DOI：10.1109/cvpr52734.2025.02413 · 被引用次数：12 · 研究领域：Computer Science

In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a single magnification level. However, this approach might not be sufficient for tasks like cancer subtype classification, tissue phenotyping, and survival analysis due to the limited level of detail that a single-resolution image can provide. Addressing this, we propose a novel multi-resolution paradigm leveraging Whole Slide Images (WSIs) to extract histology patches at multiple resolutions and generate corresponding textual descriptions through advanced CPath VLM. We introduce visual-textual alignment at multiple resolutions as well as cross-resolution alignment to establish more effective text-guided visual representations. Cross-resolution alignment using a multi-modal encoder enhances the model’s ability to capture context from multiple resolutions in histology images. Our model aims to capture a broader range of information, supported by novel loss functions, enriches feature representation, improves discriminative ability, and enhances generalization across different resolutions. Pre-trained on a comprehensive TCGA dataset with 34 million image-language pairs at various resolutions, our fine-tuned model outperforms State-Of-The-Art (SOTA) counterparts across multiple datasets and tasks, demonstrating its effectiveness in CPath. The code is available on GitHub at: https://github.com/BasitAlawode/MR...