The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches, thus eliminating the need to process the entire slide. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors (FVs) for representing features extracted from these patches, which is known for its robustness in capturing fine-grained details. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable. We have rigorously evaluated the proposed method across multiple datasets to benchmark its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The empirical results indicate that our focused analysis of select patches, combined with Fisher vector representation, not only aligns with, but at times surpasses, the classification accuracy of standard practices. Moreover, this strategy notably diminishes computational load and resource expenditure, thereby establishing an efficient and precise framework for WSI analysis in the realm of digital pathology.

翻译：数字病理学的进步，特别是通过全切片图像（WSI）的计算分析，有望显著提升诊断的精确性与效率。然而，WSI图像尺寸庞大且结构复杂，使得利用计算机对其进行分析与分类面临挑战。本研究提出了一种新颖的WSI分类方法，通过自动识别并检查信息量最大的图像块，从而无需处理整个切片。我们的方法包含两个阶段：首先，仅根据病理学意义从WSI中提取少量图像块；其次，采用Fisher向量（FV）来表示从这些图像块中提取的特征，该方法在捕获细粒度细节方面以鲁棒性著称。此方法不仅强化了WSI表征中的关键病理特征，还显著降低了计算开销，从而使流程更高效且更具可扩展性。我们已在多个数据集上对所提方法进行了严格评估，以基准测试其相对于全面WSI分析及当代弱监督学习方法的性能。实证结果表明，我们对选定图像块的聚焦分析结合Fisher向量表示，其分类准确率不仅与标准方法相当，有时甚至更优。此外，该策略显著降低了计算负荷与资源消耗，从而为数字病理学领域的WSI分析建立了一个高效且精确的框架。