Image Reconstruction.- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images.- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations.- Active MR k-space Sampling with Reinforcement Learning.- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts.- Joint reconstruction and bias field correction for undersampled MR imaging.- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping.- End-to-End Variational Networks for Accelerated MRI Reconstruction.- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning.- MRI Image Reconstruction via Learning Optimization Using Neural ODEs.- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms.- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN.- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions.- Learned Proximal Networks for Quantitative Susceptibility Mapping.- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction.- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography.- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network.- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness.- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning.- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image.- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning.- Prediction and Diagnosis.- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response.- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients.- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography.- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data.- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues.- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution.- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging.- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions.- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer.- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI.- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis.- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN.- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks.- Cross-Domain Methods and Reconstruction.- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation.- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings.- Unlearning Scanner Bias for MRI Harmonisation.- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model.- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy.- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph.- Domain Adaptation for Ultrasound Beamforming.- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction.- Domain Adaptation.- Improve Unseen Domain Generalization via Enhanced Local Color Transf