While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP (Pathology Fine-grained Language-Image Pretraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level sub-captions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in research and clinical practice.

Over the last few decades, the integration of AI-driven computational techniques into digital pathology has revolutionized survival prediction tasks. However, most existing methods in survival analysis discretize the entire survival period into predefined intervals, overlooking the inherent uncertainty in event occurrence and the heterogeneity of patient survival times. The censored data further exacerbate these challenges, amplifying uncertainty and variability. To address these limitations, we introduce the Dirichlet distribution to model discretized outputs as continuous probability distributions, providing a more accurate representation of uncertainty awareness. Building upon this foundation, we propose a universal multi-modal survival analysis loss function that leverages uncertainty-driven fusion. Our Uncertainty-Aware Multi-Modal Survival Analysis (UMSA) framework further explores the interactions between multi-scale pathological images and genomic data, providing promising insights into multi-modal survival analysis. Experimental evaluations on five publicly available datasets demonstrate that UMSA achieves state-of-the-art performance, validating its effectiveness and scalability in survival prediction tasks.