LESS：面向细胞学全切片图像筛查的标签高效多尺度学习
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening
AI总结：
本文提出LESS方法，通过变分正未标记学习利用切片级标签改进图像块特征学习，并结合多尺度裁剪与CrossViT融合信息，在仅用切片级标签的小数据集上实现了高效、准确的细胞学WSI癌症筛查。
摘要：
在计算病理学中，多实例学习（MIL）被广泛用于规避十亿像素级全切片图像（WSI）分析中的计算瓶颈。该方法通常包含两个阶段：图像块级特征提取和切片级聚合。近年来，预训练模型或自监督学习已被用于提取图像块特征，但由于忽视了切片标签所提供的任务特定监督，这些方法存在有效性低或效率低的问题。为此，我们提出了一种弱监督的标签高效WSI筛查方法，称为LESS，用于仅具有切片级标签的细胞学WSI分析，并且能够有效应用于小规模数据集。首先，我们建议使用变分正未标记（VPU）学习来揭示良性和恶性图像块的隐藏标签。我们通过利用切片级标签提供适当的监督，以改进图像块级特征的学习。其次，我们考虑了细胞学WSI中细胞排列稀疏且随机的特点。针对这一问题，我们提出了一种在多尺度上裁剪图像块的策略，并利用交叉注意力视觉Transformer（CrossViT）融合不同尺度的信息以进行WSI分类。我们两个步骤的结合实现了任务对齐，从而提升了有效性和效率。我们在包含130个样本（13,000个图像块）的尿液细胞学WSI数据集和包含212个样本（21,200个图像块）的FNAC 2019数据集上验证了所提出的标签高效方法。实验表明，所提出的LESS在尿液细胞学WSI数据集上的准确率、AUC、灵敏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%。该方法优于病理WSI上最先进的MIL方法，并实现了自动化的细胞学WSI癌症筛查。
英文摘要：
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.
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