将整张病理切片压缩为单一向量，实现高效且不变排列的分类。
Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images
- 用聚类嵌入提取切片关键区域特征，降低数据维度。
- 单向量表示可保持分类结果对切片顺序不变，提升稳定性。
- 适合大规模病理图像分析，尤其适用于资源受限场景。
全切片成像（WSI）是数字病理学的核心，提供诊断与研究所需的关键细节。然而，其吉字节级分辨率带来巨大计算挑战，限制了实际应用。本文提出新方法，利用多种编码器实现智能数据降维，并采用新型分类模型生成对排列不变的稳健表示。核心创新在于将整张WSI的复杂信息压缩为单一向量，有效捕捉分析所需关键特征。该方法显著提升WSI分析的计算效率，在无需大量算力的前提下实现更精准的病理评估。这一突破使我们能够有效应对高分辨率图像带来的挑战，推动WSI在医学诊断与研究中的可扩展、高效应用，是该领域的重大进展。
原文摘要 · Abstract (English)
Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field.
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