Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://github.com/soham-chitnis10/WSI-domain-specific.

翻译：全切片图像（WSI）或组织病理学图像广泛应用于数字病理学领域。由于图像尺寸庞大且缺乏像素级标注，WSI对临床诊断中的深度学习模型提出了巨大挑战。随着计算病理学的最新进展，基于多实例学习的新型模型被陆续提出。针对WSI的多实例学习方法需要生成图像块，并利用这些图像块的编码进行诊断。现有模型通常使用通用预训练模型（如在ImageNet上预训练的ResNet-50）进行图像块编码。近期提出的KimiaNet——一种基于TCGA切片预训练的DenseNet121模型——是领域特异性预训练模型的代表。本文展示了领域特异性预训练对WSI分类的影响。为探究其效果，我们选取了当前最先进的多实例学习模型：1）基于注意力机制的CLAM模型，2）基于自注意力机制的TransMIL模型，并评估了这些模型在检测原发性脑肿瘤（胶质瘤）时的置信度与预测性能。结果表明，领域特异性预训练不仅提升了模型的置信度，还在基于WSI的胶质瘤亚型分类中实现了新的最优性能，展现出辅助胶质瘤诊断的高度临床适用性。我们将代码与实验结果公开于https://github.com/soham-chitnis10/WSI-domain-specific。