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.

翻译：本文系统比较了多种迁移学习策略与深度学习架构在成人型弥漫性胶质瘤计算机辅助分类中的应用。我们评估了跨域ImageNet表征在组织病理图像目标域中的泛化能力，并研究了利用中等规模至大规模组织病理图像数据集，通过自监督与多任务学习方法进行域内预训练对模型的影响。此外，我们提出了一种半监督学习方法：利用微调后的模型预测全切片图像（WSI）中未标注区域的标签，随后基于真实标签与上一阶段确定的弱标签重新训练模型。该方法在平衡准确率达96.91%、F1分数达97.07%的情况下，性能优于标准域内迁移学习，同时显著减少了病理学家的标注工作量。最后，我们开发了WSI级别的可视化工具，可生成突出显示肿瘤区域的热力图，为病理学家识别WSI中信息量最大的区域提供依据。