Cytology test is effective, non-invasive, convenient, and inexpensive for
clinical cancer screening. ThinPrep, a commonly used liquid-based specimen, can
be scanned to generate digital whole slide images (WSIs) for cytology testing.
However, WSIs classification with gigapixel resolutions is highly
resource-intensive, posing significant challenges for automated medical image
analysis. In order to circumvent this computational impasse, existing methods
emphasize learning features at the cell or patch level, typically requiring
labor-intensive and detailed manual annotations, such as labels at the cell or
patch level. Here we propose a novel automated Label-Efficient WSI Screening
method, dubbed LESS, for cytology-based diagnosis with only slide-level labels.
Firstly, in order to achieve label efficiency, we suggest employing variational
positive-unlabeled (VPU) learning, enhancing patch-level feature learning using
WSI-level labels. Subsequently, guided by the clinical approach of scrutinizing
WSIs at varying fields of view and scales, we employ a cross-attention vision
transformer (CrossViT) to fuse multi-scale patch-level data and execute
WSI-level classification. 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 96.53%, 96.37%, 99.31%, 94.95% on FNAC 2019 dataset in terms of
accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art
methods and realizes automatic cytology-based bladder cancer screening.

本文提出了一种新颖的自动标签高效 WSI 筛查方法（简称 LESS），用于只具有载玻片级别标签的细胞学诊断，该方法采用 VPU 学习提高 WSI 级别标签的补丁级特征学习的效率，并使用跨注意视觉变换器（CrossViT）融合多尺度补丁级数据并执行 WSI 级分类，实现了自动细胞学膀胱癌筛查。