Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification,Medical Image Analysis

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Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification
Medical Image Analysis ( IF 10.7 ) Pub Date : 2024-05-21 , DOI: 10.1016/j.media.2024.103203
Sajid Javed ₁ , Arif Mahmood ₂ , Talha Qaiser ₃ , Naoufel Werghi ₁ , Nasir Rajpoot ₄

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The classification of gigapixel Whole Slide Images (WSIs) is an important task in the emerging area of computational pathology. There has been a surge of interest in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of cellular mutations. Most supervised 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-scale slide-level labeled training datasets that require 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. The instances (i.e., patches) from gigapixel WSIs 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 generator and cleaner 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 the unsupervised learning, we demonstrate the effectiveness of the proposed framework for weakly supervised learning and cancer subtype classification as downstream analysis. Extensive experiments on four publicly available datasets show better performance of the proposed algorithm compared to the existing state-of-the-art methods.

中文翻译：

用于数十亿像素整体幻灯片图像分类的无监督互变换学习

十亿像素全切片图像（WSI）的分类是计算病理学新兴领域的一项重要任务。人们对用于 WSI 分类的深度学习模型及其临床应用（例如癌症检测或细胞突变预测）的兴趣激增。大多数监督方法都需要病理学家专家进行昂贵且劳动密集型的手动注释。弱监督多实例学习（MIL）方法最近表现出了出色的性能；然而，他们仍然需要大规模的载玻片级标记训练数据集，需要病理学家专家仔细检查每张载玻片。在这项工作中，我们提出了一种基于互变换学习的完全无监督 WSI 分类算法。来自十亿像素 WSI 的实例（即补丁）被转换为潜在空间，然后逆变换为原始空间。利用变换损失，生成伪标签并使用变换器标签清洁器进行清洁。所提出的基于变压器的伪标签生成器和清洁器模块以无监督的方式迭代地相互训练。引入区分性学习机制来改善正常实例与癌症实例的标记。除了无监督学习之外，我们还证明了所提出的弱监督学习框架和癌症亚型分类作为下游分析的有效性。对四个公开可用数据集的广泛实验表明，与现有最先进的方法相比，所提出的算法具有更好的性能。

更新日期：2024-05-21

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