用于多千兆像素全切片图像分类的无监督互Transformer学习
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
AI总结：
针对全切片图像分类依赖人工标注的问题，提出基于互Transformer学习的无监督算法，通过伪标签生成与清理模块互训及判别式学习实现分类，在四个公开数据集上性能优于现有最优方法。
摘要：
千兆像素全切片图像（WSIs）分类是计算病理学这一新兴领域中的一项重要预测任务。目前针对WSI分类的深度学习模型研究激增，其临床应用包括癌症检测或基于WSIs预测分子突变。大多数方法都需要专业病理学家进行昂贵且费力的人工标注。弱监督多示例学习（MIL）方法近期展现出优异性能，但它们仍需要大量切片级标注的训练数据集，而这些数据集需要专业病理学家对每张切片进行仔细检查。在本研究中，我们提出了一种基于互Transformer学习的完全无监督WSI分类算法。来自千兆像素WSI的实例（即图像块）被转换到潜在空间，随后再逆转换回原始空间。利用转换损失生成伪标签，并通过Transformer标签清理器对伪标签进行清理。所提出的基于Transformer的伪标签生成模块与清理模块以无监督方式相互迭代训练。我们引入了一种判别式学习机制，以改进正常与癌变实例的标注效果。除了无监督分类，我们还验证了所提框架在作为下游分析的癌症亚型分类弱监督任务中的有效性。在四个公开可用数据集上开展的大量实验表明，与现有最优方法相比，该方法性能优异。我们计划很快公开本算法的源代码。
英文摘要：
Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs. Most methods require expensive and labor-intensive manual annotations by expert pathologists. Weakly supervised Multiple Instance Learning (MIL) methods have recently demonstrated excellent performance; however, they still require large slide-level labeled training datasets that need a careful inspection of each slide by an expert pathologist. In this work, we propose a fully unsupervised WSI classification algorithm based on mutual transformer learning. Instances from gigapixel WSI (i.e., image patches) are transformed into a latent space and then inverse-transformed to the original space. Using the transformation loss, pseudo-labels are generated and cleaned using a transformer label-cleaner. The proposed transformer-based pseudo-label generation and cleaning modules mutually train each other iteratively in an unsupervised manner. A discriminative learning mechanism is introduced to improve normal versus cancerous instance labeling. In addition to unsupervised classification, we demonstrate the effectiveness of the proposed framework for weak supervision for cancer subtype classification as downstream analysis. Extensive experiments on four publicly available datasets show excellent performance compared to the state-of-the-art methods. We intend to make the source code of our algorithm publicly available soon.
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