Search Results for author:
Found 18 papers, 7 papers with code
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject.
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
Neural Proximal Gradient Descent for Compressive Imaging
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.
Image and Video Processing
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
200x Low-dose PET Reconstruction using Deep Learning
Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose.
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
Uncertainty Quantification in Deep MRI Reconstruction
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.
Degrees of Freedom Analysis of Unrolled Neural Networks
Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems.
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms
We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem.
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks.
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time.
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws.
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks.
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
We propose using a coordinate network decoder for the task of super-resolution in MRI.
