We provide in this paper a comprehensive comparison of various transfer
learning strategies and deep learning architectures for computer-aided
classification of adult-type diffuse gliomas. We evaluate the generalizability
of out-of-domain ImageNet representations for a target domain of
histopathological images, and study the impact of in-domain adaptation using
self-supervised and multi-task learning approaches for pretraining the models
using the medium-to-large scale datasets of histopathological images. A
semi-supervised learning approach is furthermore proposed, where the fine-tuned
models are utilized to predict the labels of unannotated regions of the whole
slide images (WSI). The models are subsequently retrained using the
ground-truth labels and weak labels determined in the previous step, providing
superior performance in comparison to standard in-domain transfer learning with
balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the
pathologist's efforts for annotation. Finally, we provide a visualization tool
working at WSI level which generates heatmaps that highlight tumor areas; thus,
providing insights to pathologists concerning the most informative parts of the
WSI.

我们在本文中对用于成年型弥漫性胶质瘤的计算机辅助分类的各种迁移学习策略和深度学习架构进行了全面比较。我们评估了针对组织病理学图像目标领域的域外 ImageNet 表示的泛化能力，并研究了使用自监督和多任务学习方法进行预训练的在域适应的影响。此外，我们提出了一种半监督学习方法，其中利用微调模型来预测整张幻灯片图像的未标注区域的标签。在使用先前步骤中确定的真实标签和弱标签进行重新训练后，模型表现优异，平衡准确率为 96.91％，F1 分数为 97.07％，并最大程度地减轻了病理学家的注释工作量。最后，我们提供了一种工作在幻灯片图像级别的可视化工具，生成能突出肿瘤区域的热图，从而为病理学家提供幻灯片图像中最有信息的部分的见解。