Search Results for author: Thomas Yu

Found 8 papers, 1 papers with code

Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders

no code implementations29 Aug 2022 Hamza Kebiri, Gabriel Girard, Yasser Aleman-Gomez, Thomas Yu, Andras Jakab, Erick Jorge Canales-Rodriguez, Meritxell Bach Cuadra

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.


Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

no code implementations29 Jan 2022 Thomas Yu, Tom Hilbert, Gian Franco Piredda, Arun Joseph, Gabriele Bonanno, Salim Zenkhri, Patrick Omoumi, Meritxell Bach Cuadra, Erick Jorge Canales-Rodríguez, Tobias Kober, Jean-Philippe Thiran

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.

Anatomy Denoising +1

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

1 code implementation5 Jul 2021 Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, James Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, Ahmed W. Moawad, Luiz Otavio Coelho, Olivia McDonnell, Elka Miller, Fanny E. Moron, Mark C. Oswood, Robert Y. Shih, Loizos Siakallis, Yulia Bronstein, James R. Mason, Anthony F. Miller, Gagandeep Choudhary, Aanchal Agarwal, Cristina H. Besada, Jamal J. Derakhshan, Mariana C. Diogo, Daniel D. Do-Dai, Luciano Farage, John L. Go, Mohiuddin Hadi, Virginia B. Hill, Michael Iv, David Joyner, Christie Lincoln, Eyal Lotan, Asako Miyakoshi, Mariana Sanchez-Montano, Jaya Nath, Xuan V. Nguyen, Manal Nicolas-Jilwan, Johanna Ortiz Jimenez, Kerem Ozturk, Bojan D. Petrovic, Chintan Shah, Lubdha M. Shah, Manas Sharma, Onur Simsek, Achint K. Singh, Salil Soman, Volodymyr Statsevych, Brent D. Weinberg, Robert J. Young, Ichiro Ikuta, Amit K. Agarwal, Sword C. Cambron, Richard Silbergleit, Alexandru Dusoi, Alida A. Postma, Laurent Letourneau-Guillon, Gloria J. Guzman Perez-Carrillo, Atin Saha, Neetu Soni, Greg Zaharchuk, Vahe M. Zohrabian, Yingming Chen, Milos M. Cekic, Akm Rahman, Juan E. Small, Varun Sethi, Christos Davatzikos, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva-Meyer, John B. Freymann, Justin S. Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Bjoern Menze, Adam E. Flanders, Spyridon Bakas

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.

Benchmarking Brain Tumor Segmentation +2

Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images

no code implementations6 Apr 2021 Mohammad Saeed Rad, Thomas Yu, Behzad Bozorgtabar, Jean-Philippe Thiran

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.

SSIM Super-Resolution +1

Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

no code implementations6 Jul 2020 Mohammad Saeed Rad, Thomas Yu, Claudiu Musat, Hazim Kemal Ekenel, Behzad Bozorgtabar, Jean-Philippe Thiran

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.

Image Super-Resolution

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