Hepato-LLaVA：一种基于稀疏拓扑包注意力机制的专家多模态大语言模型，用于肝细胞病理分析的整张图像
Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images
AI总结：
Hepato-LLaVA通过稀疏拓扑包注意力机制和HepatoPathoVQA数据集，实现肝细胞病理分析的高精度诊断与描述。
摘要：
肝细胞癌的诊断严重依赖于对十亿像素整张图像的解读。然而，当前的计算方法受到固定分辨率处理机制和低效特征聚合的限制，这不可避免地导致严重的信息丢失或高特征冗余。为了解决这些挑战，我们提出了Hepato-LLaVA，一种专门用于细粒度肝细胞病理分析的多模态大语言模型。我们引入了一种新的稀疏拓扑包注意力机制，该机制明确建模二维组织拓扑。该机制能够有效地将局部诊断证据聚合到语义摘要标记中，同时保持全局上下文。此外，为了克服多尺度数据不足的问题，我们提出了HepatoPathoVQA，一个由33000个层次化结构的问题-答案对组成的临床验证数据集，由专家病理科医生验证。我们的实验表明，Hepato-LLaVA在肝细胞癌诊断和描述任务上实现了最先进的性能，显著优于现有方法。我们的代码和实现细节可在https://pris-cv.github.io/Hepto-LLaVA/上获得。
英文摘要：
Hepatocellular Carcinoma diagnosis relies heavily on the interpretation of gigapixel Whole Slide Images. However, current computational approaches are constrained by fixed-resolution processing mechanisms and inefficient feature aggregation, which inevitably lead to either severe information loss or high feature redundancy. To address these challenges, we propose Hepato-LLaVA, a specialized Multi-modal Large Language Model designed for fine-grained hepatocellular pathology analysis. We introduce a novel Sparse Topo-Pack Attention mechanism that explicitly models 2D tissue topology. This mechanism effectively aggregates local diagnostic evidence into semantic summary tokens while preserving global context. Furthermore, to overcome the lack of multi-scale data, we present HepatoPathoVQA, a clinically grounded dataset comprising 33K hierarchically structured question-answer pairs validated by expert pathologists. Our experiments demonstrate that Hepato-LLaVA achieves state-of-the-art performance on HCC diagnosis and captioning tasks, significantly outperforming existing methods. Our code and implementation details are available at https://pris-cv.github.io/Hepto-LLaVA/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
