Multi-dimensional medical data are rapidly collected to enhance healthcare. With the recent advance in artificial intelligence, deep learning techniques have been widely applied to medical images, constituting a significant proportion of medical data. The techniques of automated medical image analysis have the potential to benefit general clinical procedures, e.g., disease screening, malignancy diagnosis, patient risk prediction, and surgical planning. Although preliminary success takes place, the robustness of these approaches requires to be cautiously validated and sufficiently guaranteed before their application to real-world clinical problems.
In this thesis, we propose different approaches to improve the robustness of deep learning algorithms for automated medical image analysis. (i) In terms of network architecture, we leverage the advantages of both 2D and 3D networks, and propose an alternative 2.5D approach for 3D organ segmentation. (ii) To improve data efficiency and utilize large-scale unlabeled medical data, we propose a unified framework for semi-supervised medical image segmentation and domain adaptation. (iii) For the safety-critical applications, we design a unified approach for failure detection and anomaly segmentation. (iv) We study the problem of Federated Learning, which enables collaborative learning and preserves data privacy, and improve the robustness of the algorithm in the non-i.i.d setting. (v) We incorporate multi-phase information for more accurate pancreatic tumor detection. (vi) Finally, we show our discovery for potential pancreatic cancer screening on non-contrast CT scans which outperform expert radiologists
