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Found 7 papers, 4 papers with code
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines.
Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs).
Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction.
Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
By synthetic experiments, we further show the capability of our approach in learning an explicit anatomical shape representation.
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition.
