A curated list of materials for Deep Learning in medical applications.

• Overview papers
• Magnetic resonance imaging
• Cardiac deep learning
• Segmentations
• Challenges

1. A Survey on Deep Learning in Medical Image Analysis, Litjens et al. (2017) - paper reviews over 300 works on deep learning in different branches of medical analysis.
2. Medical Image Segmentation Using Deep Learning: A Survey, Lei et al. (2020) - review of deep learning based approaches for medical image segmentation building overview of state-of-the-art research in this area.

1. Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction, Souza et al. (2019) - U-net like architecture for MRI reconstruction.
2. State-of-the-art machine learning reconstruction in 2020: results of the second fastMRI challenge, Muckley et al. (2020) - results of fastMRI challenge 2020.

1.Deep Learning for Cardiac Image Segmentation: A Review - Chen et al. (2020) - overview of cardiac segmentation research.

1. U-net: Convolutional networks for biomedical image segmentation - Ronneberger et al. (2015) - proposes the U-Net architecture for medical segmentation, which is now popular in other branches of medical imaging.

1. FastMRI - challenge for AI techniques, in particular deep learning methods, for increasing the speed of MRI data aquisition.
2. RealNoiseMRI - challenge for increasing the speed of MRI data aquisition with real noise data.
3. 2021 Kidney and Kidney Tumor Segmentation - kidney and kidney tumor segmentation challenge as a MICCAI challenge.
4. Fetal Brain Tissue Annotation and Segmentation Challenge - segmentation challenge on fetal MRI data as a MICCAI challenge.