Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

深度学习越来越多地用于医学成像，改进了处理链的许多步骤，从采集到分割，从异常检测到结果预测。然而，仍然存在重大挑战：（i）基于图像的诊断取决于局部模式之间的空间关系，卷积和池化通常无法充分捕捉；(ii) 数据增强，实际上是学习三维姿态不变性的方法，需要指数级多的点才能实现稳健的改进；(iii) 标记的医学图像比未标记的要少得多，特别是对于异质的病理病例；(iv) 磁共振成像等扫描技术可能既慢又昂贵，通常没有在线学习能力来专注于临床感兴趣的区域。为了应对这些挑战，