双查询多示例学习用于基于动态元嵌入的肿瘤分类
Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
AI总结：
提出DQ-MIL，一种基于动态元嵌入和双查询感知器的多示例学习架构，结合自蒸馏与注意力机制，在三个组织病理学数据集上实现高达10%的性能提升。
摘要：
全切片图像（WSI）评估是癌症诊断和治疗计划中一个具有挑战性且至关重要的步骤。WSI需要高倍率以支持亚细胞水平分析。在千兆像素WSI的背景下，对斑块甚至像素级分类进行精确标注既繁琐又需要领域专家。另一方面，粗粒度标签易于获取，这使得WSI分类成为多示例学习（MIL）的理想应用场景。在我们的工作中，我们提出了一种新颖的基于嵌入的双查询MIL流程（DQ-MIL）。我们在嵌入和聚合两个步骤均做出了贡献。由于通用视觉特征表示尚不可用，嵌入模型目前在泛化性方面受到限制。通过我们的工作，我们探索了基于前沿自监督预训练模型的动态元嵌入在MIL中的潜力。此外，我们提出了一种新的MIL架构，能够将MIL注意力与相关的自注意力相结合。我们方法的双查询感知器设计使我们能够利用自蒸馏的概念，并在低数据场景下结合小模型的优势与更大模型的丰富特征表示。我们在三个组织病理学数据集上展示了我们方法的优越性能，相较于最先进的方法，我们展示了高达10%的提升。
英文摘要：
Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysis. Precise annotations for patch- or even pixel-level classifications in the context of gigapixel WSIs are tedious to acquire and require domain experts. Coarse-grained labels, on the other hand, are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL). In our work, we propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL). We contribute to both the embedding and aggregation steps. Since all-purpose visual feature representations are not yet available, embedding models are currently limited in terms of generalizability. With our work, we explore the potential of dynamic meta-embedding based on cutting-edge self-supervised pre-trained models in the context of MIL. Moreover, we propose a new MIL architecture capable of combining MIL-attention with correlated self-attention. The Dual-Query Perceiver design of our approach allows us to leverage the concept of self-distillation and to combine the advantages of a small model in the context of a low data regime with the rich feature representation of a larger model. We demonstrate the superior performance of our approach on three histopathological datasets, where we show improvement of up to 10% over state-of-the-art approaches.
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。
