领域适应无需计算负担的高效全滑动图像分析
Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysis
AI总结：
本文提出EfficientWSI方法，结合参数高效微调和多实例学习，解决全滑动图像分析中的计算负担和领域特定特性捕捉问题，通过实验验证其在多个任务中的有效性。
摘要：
对全滑动图像（WSI）进行计算分析的方法能够通过支持病理科医生检测和分类肿瘤来实现早期诊断和治疗。然而，WSI的极高分辨率使得端到端训练在与典型图像分析任务相比时变得不切实际。为了解决这个问题，大多数方法使用在自然图像数据集（如ImageNet）上预训练的特征提取器来获得全滑动图像的固定表示，然后结合多实例学习（MIL）进行下游任务。这些特征提取器通常在自然图像数据集上预训练，无法捕捉领域特定的特征。虽然在病理学数据上进行领域特定的预训练可以产生更相关特征表示，但仍然计算成本高且无法捕捉领域内的任务特定特征。为了解决领域特定预训练的计算成本和缺乏任务特定性的缺点，我们提出EfficientWSI（eWSI），这是参数高效微调（PEFT）和多实例学习（MIL）的精心整合，使在WSI任务上进行端到端训练成为可能。我们在Camelyon16、TCGA和BRACS数据集上评估了eWSI在七个WSI级别任务上的表现。我们的结果表明，当eWSI与ImageNet特征提取器结合时，其分类性能强大，与使用领域内特征提取器的MIL相当或优于其，减轻了对大量领域内预训练的需求。此外，当eWSI与领域内特征提取器结合时，在大多数情况下进一步提高了分类性能，证明了其在有益情况下捕捉任务特定信息的能力。我们的发现表明，eWSI为WSI任务提供了一条任务定向且计算高效的路径，为计算病理学中的任务特定学习提供了有前景的方向。
英文摘要：
Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classification of tumors. However, the extremely high resolution of WSIs makes end-to-end training impractical compared to typical image analysis tasks. To address this, most approaches use pre-trained feature extractors to obtain fixed representations of whole slides, which are then combined with Multiple Instance Learning (MIL) for downstream tasks. These feature extractors are typically pre-trained on natural image datasets such as ImageNet, which fail to capture domain-specific characteristics. Although domain-specific pre-training on histopathology data yields more relevant feature representations, it remains computationally expensive and fail to capture task-specific characteristics within the domain. To address the computational cost and lack of task-specificity in domain-specific pre-training, we propose EfficientWSI (eWSI), a careful integration of Parameter-Efficient-Fine-Tuning (PEFT) and Multiple Instance Learning (MIL) that enables end-to-end training on WSI tasks. We evaluate eWSI on seven WSI-level tasks over Camelyon16, TCGA and BRACS datasets. Our results show that eWSI when applied with ImageNet feature extractors yields strong classification performance, matching or outperforming MILs with in-domain feature extractors, alleviating the need for extensive in-domain pre-training. Furthermore, when eWSI is applied with in-domain feature extractors, it further improves classification performance in most cases, demonstrating its ability to capture task-specific information where beneficial. Our findings suggest that eWSI provides a task-targeted, computationally efficient path for WSI tasks, offering a promising direction for task-specific learning in computational pathology.
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