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 impact 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.

该论文研究了域特定预训练模型对神经胶质母细胞瘤分类的影响和应用性，通过考虑目前最先进的多实例学习模型，CLAM 和 TransMIL，并评估模型的置信度和预测性能，表明域特定预训练有助于提高模型置信度并实现 WSI 基础的神经胶质母细胞瘤亚型分类的最新最佳性能。