用于全切片图像分类的难负样本挖掘
Hard Negative Sample Mining for Whole Slide Image Classification
AI总结：
本文针对弱监督全切片图像分类，提出在微调阶段挖掘难负样本并结合新的图像块级排序损失，以提升特征表示质量、降低训练成本，并在两个公开数据集上验证了有效性。
摘要：
由于缺乏图像块级别的标签且计算成本高昂，弱监督全切片图像（WSI）分类极具挑战性。当前最先进的方法利用自监督的图像块级特征表示进行多实例学习（MIL）。近年来，已有方法提出通过伪标签在下游任务上微调特征表示，但大多侧重于选择高质量的正样本图像块。在本文中，我们提出在微调过程中挖掘难负样本。这使我们能够获得更好的特征表示并降低训练成本。此外，我们提出了一种新颖的MIL图像块级排序损失，以更好地利用这些难负样本。在两个公开数据集上的实验证明了所提方法的有效性。我们的代码可在 https://github.com/winston52/HNM-WSI 获取。
英文摘要：
Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs. State-of-the-art methods use self-supervised patch-wise feature representations for multiple instance learning (MIL). Recently, methods have been proposed to fine-tune the feature representation on the downstream task using pseudo labeling, but mostly focusing on selecting high-quality positive patches. In this paper, we propose to mine hard negative samples during fine-tuning. This allows us to obtain better feature representations and reduce the training cost. Furthermore, we propose a novel patch-wise ranking loss in MIL to better exploit these hard negative samples. Experiments on two public datasets demonstrate the efficacy of these proposed ideas. Our codes are available at https://github.com/winston52/HNM-WSI
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。
