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Found 12 papers, 6 papers with code
A Benchmark of Domain-Adapted Large Language Models for Generating Brief Hospital Course Summaries
To enable the adaptation of LLMs for BHC synthesis, we introduce a novel benchmark consisting of a pre-processed dataset extracted from MIMIC-IV notes, encapsulating clinical note, and brief hospital course (BHC) pairs.
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.
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.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
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).
Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography
Extracting quantitative body composition measures manually from CT scans is a cumbersome and time-consuming task.
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.
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
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 medical imaging field.
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.
Measuring Robustness in Deep Learning Based Compressive Sensing
In order to understand the sensitivity to such perturbations, in this work, we measure the robustness of different approaches for image reconstruction including trained and un-trained neural networks as well as traditional sparsity-based methods.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks
High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications.
