WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion

Whole slide images (WSIs) in computational pathology (CPath) pose a major computational challenge due to their gigapixel scale, often requiring the processing of tens to hundreds of thousands of high-resolution patches per slide. This results in prohibitive encoding costs, with preprocessing and training times extending to days or even weeks-making WSI encoding the most significant bottleneck in real-world deployment. In this work, we propose WISE-FUSE, an adaptive WSI encoding framework that leverages pathology-domain vision-language models and large language models to address this challenge by selectively processing diagnostically relevant regions. WISE-FUSE first computes similarity scores between low-resolution patches and class-specific textual descriptions using a knowledge distillation mechanism that preserves fine-grained diagnostic features. Based on these similarity scores, we select a small subset of informative regions for the target task, which quickly eliminates irrelevant patches at the coarse level. The corresponding high-resolution patches are then selectively encoded and fused with textual embeddings to reinforce diagnostic context. Extensive experiments demonstrate that WISE-FUSE reduces WSI encoding time by over threefold while achieving diagnostic performance comparable to or surpassing that of exhaustive patch processing, offering a scalable and practical solution for CPath.

Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM's limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM's decoder. The experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. The code is available at \url{https://github.com/hvcl/SAM_Interactive_Histopathology}