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Found 6 papers, 1 papers with code
Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study
This paper investigates the impact of domain-specific model fine-tuning and of reasoning mechanisms on the performance of question-answering (Q&A) systems powered by large language models (LLMs) and Retrieval-Augmented Generation (RAG).
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
A Multiagent CyberBattleSim for RL Cyber Operation Agents
In particular, training a blue agent jointly with a red agent increases the blue agent's capability to thwart sophisticated red agents.
Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)
GAN-CEST images from a brain-tumor subject yielded a semi-solid volume fraction and exchange rate NRMSE of 3. 8$\pm$1. 3% and 4. 6$\pm$1. 3%, respectively, and SSIM of 96. 3$\pm$1. 6% and 95. 0$\pm$2. 4%, respectively.
Generative Adversarial Network
Magnetic Resonance Fingerprinting
+1
Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging
Training deep learning models on cardiac magnetic resonance imaging (CMR) can be a challenge due to the small amount of expert generated labels and inherent complexity of data source.
Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus
The segmentation and network features are used to train a model for NPH prediction.
