SEW：自校准增强的全切片病理图像分析
SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis
AI总结：
本文提出SEW自校准增强框架，通过全局分支、焦点预测器、细节分支及特征一致性约束，高效融合全切片病理图像的全局与局部信息，提升分级、预后及标志物发现的准确性与可解释性。
摘要：
病理图像被视为癌症诊断和治疗的“金标准”，其中千兆像素图像提供了丰富的组织和细胞信息。现有方法无法同时高效提取全局结构和局部细节特征，以进行全面的病理图像分析。为解决这些局限，我们提出了一种用于全切片病理图像分析的自校准增强框架，包含三个组成部分：全局分支、焦点预测器和细节分支。全局分支首先利用病理缩略图进行分类，而焦点预测器则基于全局分支最后一层的特征识别与分类相关的区域。随后，细节提取分支评估放大后的区域是否对应病变区域。最后，全局分支与细节分支之间的特征一致性约束确保全局分支关注适当区域，并提取足够的判别性特征用于最终识别。这些聚焦的判别性特征从特征簇唯一性和组织空间分布的角度，对于发现新的预后肿瘤标志物具有重要价值。大量实验结果表明，所提出的框架能够为病理分级和预后任务快速提供准确且可解释的结果。
英文摘要：
Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To address these limitations, we propose a self-calibration enhanced framework for whole slide pathology image analysis, comprising three components: a global branch, a focus predictor, and a detailed branch. The global branch initially classifies using the pathological thumbnail, while the focus predictor identifies relevant regions for classification based on the last layer features of the global branch. The detailed extraction branch then assesses whether the magnified regions correspond to the lesion area. Finally, a feature consistency constraint between the global and detail branches ensures that the global branch focuses on the appropriate region and extracts sufficient discriminative features for final identification. These focused discriminative features prove invaluable for uncovering novel prognostic tumor markers from the perspective of feature cluster uniqueness and tissue spatial distribution. Extensive experiment results demonstrate that the proposed framework can rapidly deliver accurate and explainable results for pathological grading and prognosis tasks.
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。
