  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.
