Search Results for author:
Found 8 papers, 1 papers with code
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders
Interestingly, the fractional anisotropy and, to a lesser extent, the mean diffusivity, are best recovered in missing slices by using the autoencoder trained with SH coefficients.
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models
Discussion We demonstrated the feasibility of using the NLP Sandbox to conduct a multi-site evaluation of clinical text de-identification models without the sharing of data.
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability.
FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images
Addressing both issues, we propose a simple yet universal approach to improve the perceptual quality of the HR prediction from a pre-trained SR network on a given LR input by further fine-tuning the SR network on a subset of images from the training dataset with similar patterns of activation as the initial HR prediction, with respect to the filters of a feature extractor.
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs.
