Mattias P. Heinrich

TL;DR: A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).

TL;DR: Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency.

TL;DR: A modality independent neighbourhood descriptor (MIND), based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising, is proposed and applied for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans.

TL;DR: In this article, a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularization model, which is trained end-to-end, encourages models to follow the global anatomical properties of the underlying anatomy via learnt non-linear representations of the shape.

TL;DR: This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.