Gliomas are the most frequent primary brain tumors in adults. Glioma change detection aims at finding the relevant parts of the image that change over time. Although Deep Learning (DL) shows promising performances in similar change detection tasks, the creation of large annotated datasets represents a major bottleneck for supervised DL applications in radiology. To overcome this, we propose a combined use of weak labels (imprecise, but fast-to-create annotations) and Transfer Learning (TL). Specifically, we explore inductive TL, where source and target domains are identical, but tasks are different due to a label shift: our target labels are created manually by three radiologists, whereas our source weak labels are generated automatically from radiology reports via NLP. We frame knowledge transfer as hyperparameter optimization, thus avoiding heuristic choices that are frequent in related works. We investigate the relationship between model size and TL, comparing a low-capacity VGG with a higher-capacity ResNeXt model. We evaluate our models on 1693 T2-weighted magnetic resonance imaging difference maps created from 183 patients, by classifying them into stable or unstable according to tumor evolution. The weak labels extracted from radiology reports allowed us to increase dataset size more than 3-fold, and improve VGG classification results from 75% to 82% AUC. Mixed training from scratch led to higher performance than fine-tuning or feature extraction. To assess generalizability, we ran inference on an open dataset (BraTS-2015: 15 patients, 51 difference maps), reaching up to 76% AUC. Overall, results suggest that medical imaging problems may benefit from smaller models and different TL strategies with respect to computer vision datasets, and that report-generated weak labels are effective in improving model performances. Code, in-house dataset and BraTS labels are released.
翻译:Gliomas 是成人最常见的脑肿瘤。 Glioma 变化检测旨在寻找长期变化图像的相关部分。 Glioma 检测旨在寻找长期变化图像的相关部分。 虽然深层学习( DL) 显示类似变化检测任务中有良好的表现, 创建大型附加说明的数据集是受监管的 DL 应用放射学中的主要瓶颈。 为了克服这一点, 我们提议合并使用薄弱标签( 简化, 但快速创建说明) 和 转移学习( TL ) 。 具体地说, 我们探索导入式 TLL, 其源和目标领域相同, 但任务因标签变化而不同: 我们的目标标签是由三名放射学家手工创建的, 而我们的源性弱标签则是通过 NLP 的放射报告自动生成的。 我们把知识转移作为超光度优化, 从而避免相关作品中经常出现的超度选择。 我们用一个低容量的 VGGG- GL, 与一个更高容量的SNeXt 模型相比, 我们用1693 T级的磁感应变缩模型来评估模型的模型, 从183个病人的Seral 总体变变换数据变换数据, 将数据升级为稳定数据变换为稳定数据变换为稳定数据。