755 citations · 912 across the 18 of their papers we have counts for
18 papers
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Dave Van Veen, Cara Van Uden, Louis Blankemeier +16
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large langua…
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Scott L. Fleming, Alejandro Lozano, William J. Haberkorn +27
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burd…
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
Dave Van Veen, Cara Van Uden, Maayane Attias +9
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain ad…
Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography
Louis Blankemeier, Arjun Desai, Juan Manuel Zambrano Chaves +11
Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to e…
DDM: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Tiange Xiang, Mahmut Yurt, Ali B Syed +2
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting…
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai +8
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medic…
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Beliz Gunel, Arda Sahiner, Arjun D. Desai +4
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.…
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
Arjun D Desai, Andrew M Schmidt, Elka B Rubin +9
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and d…
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Arjun D Desai, Beliz Gunel, Batu M Ozturkler +6
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstructio…
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Arjun D Desai, Batu M Ozturkler, Christopher M Sandino +7
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled)…
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
Anirudh Joshi, Sabri Eyuboglu, Shih-Cheng Huang +5
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches…
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
Joseph Paul Cohen, Rupert Brooks, Sovann En +4
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imag…
Measuring Robustness in Deep Learning Based Compressive Sensing
Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imagi…
Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Kevin A. Thomas, Dominik Krzemiński, Łukasz Kidziński +7
Objective: We evaluate a fully-automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin echo (MESE) MRI. We…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks
Arjun D. Desai, Garry E. Gold, Brian A. Hargreaves +1
High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications…
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee +2
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and resea…