Despite an over-30-year-old research history, detection and segmentation in medical images are still open problems, which have recently seen spectacular advances thanks to machine learning and, in particular, to deep learning. In this context, we will address these challenges:
- integration of domain knowledge into deep learning, such as hard shape constraints to guarantee the anatomical consistency of any outputs, joint localization and segmentation to improve accuracy, pathological-class imbalance via data-augmentation techniques through generative networks;
- detection and segmentation of large heterogeneous datasets of 3D images via weakly supervised or unsupervised deep learning methods through domain adaptation or transfer learning;
- enhancement of multi-modality statistical atlas-based methods via state-of-the-art feature extraction techniques, improving their accuracy (data-to-atlas transfer or inter-modality domain adaptation) and confidence (uncertainty estimation).