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Found 6 papers, 3 papers with code
RadRotator: 3D Rotation of Radiographs with Diffusion Models
This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference.
CONFLARE: CONFormal LArge language model REtrieval
Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses.
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Deep learning techniques, despite their potential, often suffer from a lack of reproducibility and generalizability, impeding their clinical adoption.
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
This study examines the impact of synthetic data supplementation, using diffusion models, on the performance of deep learning (DL) classifiers for CXR analysis.
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
Although the majority of inpainting techniques for medical imaging data use generative adversarial networks (GANs), the performance of these algorithms is frequently suboptimal due to their limited output variety, a problem that is already well-known for GANs.
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks
There is a growing demand for high-resolution (HR) medical images in both the clinical and research applications.
