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Found 11 papers, 8 papers with code
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
We propose a generative model that compresses discrete representations of each sequence to estimate the Gaussian distribution of vector-quantized common (VQC) latent space between multiple sequences.
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons.
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities.
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies.
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Deformable Registration of Brain MR Images via a Hybrid Loss
Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields.
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning.
Accelerator Physics
An Auto-Context Deformable Registration Network for Infant Brain MRI
Deformable image registration is fundamental to longitudinal and population analysis.
