释放几何的无限力量：用于全切片组织病理图像分析的新型几何感知Transformer（GOAT）
Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis
AI总结：
针对全切片图像分析中几何信息利用不足的问题，提出几何感知Transformer（GOAT）弱监督框架，结合上下文感知注意力机制，利用肿瘤微环境几何特征提升病理图像分析性能。
摘要：
组织病理学分析对于癌症的诊断和预后具有重要意义，然而，由于千兆像素全切片图像（WSIs）的巨大异质性和病理特征的精妙表征，它面临着巨大挑战。然而，近期方法尚未充分利用WSIs中的几何表征，而几何表征在疾病诊断中具有重要意义。因此，我们提出了一种新型弱监督框架——几何感知Transformer（GOAT），在该框架中，我们促使模型关注肿瘤微环境内的几何特征，这些特征往往作为强有力的指标。此外，我们设计了一种上下文感知注意力机制，以提取并增强WSIs内的形态学特征。
英文摘要：
The histopathology analysis is of great significance for the diagnosis and prognosis of cancers, however, it has great challenges due to the enormous heterogeneity of gigapixel whole slide images (WSIs) and the intricate representation of pathological features. However, recent methods have not adequately exploited geometrical representation in WSIs which is significant in disease diagnosis. Therefore, we proposed a novel weakly-supervised framework, Geometry-Aware Transformer (GOAT), in which we urge the model to pay attention to the geometric characteristics within the tumor microenvironment which often serve as potent indicators. In addition, a context-aware attention mechanism is designed to extract and enhance the morphological features within WSIs.
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。
