Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI). However, traditional pure vision models face challenges of redundant feature extraction, whereas existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy. To overcome these issues, we propose two innovative strategies: the mixed task-guided feature enhancement, which directs feature extraction toward lesion-related details across scales, and the prompt-guided detail feature completion, which integrates coarse- and fine-grained features from WSI based on specific prompts without compromising inference speed. Leveraging a comprehensive dataset of 490,000 samples from diverse pathology tasks-including cancer detection, grading, vascular and neural invasion identification, and so on-we trained the pathology-specialized LVLM, OmniPath. Extensive experiments demonstrate that this model significantly outperforms existing methods in diagnostic accuracy and efficiency, offering an interactive, clinically aligned approach for auxiliary diagnosis in a wide range of pathology applications.

翻译：病理诊断对于确定疾病特征、指导治疗和评估预后至关重要，其高度依赖于对高分辨率全切片图像（WSI）进行详细的多尺度分析。然而，传统的纯视觉模型面临特征提取冗余的挑战，而现有的大视觉语言模型（LVLM）则受限于输入分辨率约束，阻碍了其效率与准确性。为克服这些问题，我们提出了两种创新策略：混合任务引导的特征增强，将特征提取导向跨尺度的病变相关细节；以及提示引导的细节特征补全，在不影响推理速度的前提下，基于特定提示整合来自WSI的粗粒度与细粒度特征。利用来自多种病理任务（包括癌症检测、分级、血管与神经侵犯识别等）的490,000个样本组成的综合数据集，我们训练了病理专用的大视觉语言模型OmniPath。大量实验表明，该模型在诊断准确性和效率上显著优于现有方法，为广泛的病理学应用提供了一种交互式、与临床需求对齐的辅助诊断途径。