In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.

翻译：在计算病理学中，多实例学习（MIL）被广泛用于突破千兆像素全切片图像（WSI）分析中的计算瓶颈。该方法通常包含两个阶段：斑块级特征提取与切片级聚合。当前，研究者常采用预训练模型或自监督学习提取斑块特征，但由于忽视了切片标签提供的任务特定监督信息，这些方法存在效果不佳或效率低下的问题。本文提出一种仅需切片级标签的弱监督标签高效WSI筛选方法（LESS），可有效应用于小规模数据集。首先，我们提出采用变分正未标记（VPU）学习来揭示良恶性斑块的隐藏标签，通过利用切片级标签提供适当监督，从而改进斑块级特征学习。其次，针对细胞学WSI中细胞稀疏且随机分布的特点，我们提出多尺度斑块裁剪策略，并利用交叉注意力视觉Transformer（CrossViT）融合不同尺度信息进行WSI分类。两步方法的结合实现了任务对齐，提升了有效性与效率。我们在包含130个样本（13,000个斑块）的尿液细胞学WSI数据集和包含212个样本（21,200个斑块）的FNAC 2019数据集上验证了所提标签高效方法。实验结果表明，LESS在尿液细胞学WSI数据集上的准确率、AUC、敏感度和特异度分别达到84.79%、85.43%、91.79%和78.30%，在FNAC 2019数据集上分别达到96.88%、96.86%、98.95%和97.06%。该方法在病理WSI分析中优于现有最优MIL方法，实现了细胞学WSI癌症筛查的自动化。