| CODE | TITLE | OUTPUT |
|---|---|---|
| MI 01 |
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction |
|
| MI 02 |
A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation |
|
| MI 03 |
Anatomically Constrained Neural Networks (ACNNs) Application to Cardiac Image Enhancement and Segmentation |
|
| MI 04 |
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction |
|
| MI 05 |
End-to-End Adversarial Retinal Image Synthesis |
|
| MI 06 |
Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models |
|
| MI 07 |
Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance Models |
|
| MI 08 |
Magnetic Resonance RF Pulse Design by Optimal Control with Physical Constraints |
|
| MI 09 |
Multimodal MR Synthesis via Modality-Invariant Latent Representation |
|
| MI 10 |
Robust Unmixing of Dynamic Sequences Using Regions of Interest |
|
| MI 11 |
Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes |
|
| MI 12 |
Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors |
|
| MI 13 |
Synergistic PET and SENSE MR Image Reconstruction Using Joint Sparsity Regularization |
|
| MI 14 |
Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data an example from lung cancer |
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