Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation.

[BibT_eX]

[DOI]

Francesco Santini

Jakob Wasserthal

Claudia Weidensteiner

CoRR, 2023

2022

Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks.

[BibT_eX]

[DOI]

Dave Van Veen

Rogier van der Sluijs

CoRR, 2022

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2022

GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction.

[BibT_eX]

[DOI]

Christopher M. Sandino

CoRR, 2022

VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Monarch: Expressive Structured Matrices for Efficient and Accurate Training.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising.

[BibT_eX]

[DOI]

Arjun D. Desai

Batu M. Ozturkler

Christopher M. Sandino

CoRR, 2021

SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation.

[BibT_eX]

[DOI]

Arjun D. Desai

Andrew M. Schmidt

Elka B. Rubin

Christopher M. Sandino

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.

[BibT_eX]

[DOI]