A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

Instantly improve your training performance of TensorFlow models with just 2 lines of code!

TensorLy: Tensor Learning in Python.

Implementation of Bayesian Non-negative Matrix Factorization using Variational Inference with TensorFlow Probability

Collection of reproducible deep learning for compressive sensing

Interpretability Methods for tf.keras models with Tensorflow 2.x

Latex code for making neural networks diagrams

some bravo or inspiring research works on the topic of curriculum learning

ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, ach…

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Simple and comprehensive tutorials in TensorFlow

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

Use Fourier transform to learn operators in differential equations.

Official Pytorch Implementation of "Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Network" (accepted by MICCAI 2019)

Keras documentation, hosted live at keras.io

Implementation related to the paper "Complex-Valued Convolutional Neural Networks for MRI Reconstruction" by Elizabeth K. Cole et. al; Toolbox for complex-valued convolution and activation function…

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Joint training of denoising and segmentation.

MICCAI 2020 Open Source Papers

A large-scale dataset of both raw MRI measurements and clinical MRI images

Resources for learning the principles of magnetic resonance imaging (MRI)

SSNMF models and multiplicative update methods.

Codes for 'Stationary Activations for Uncertainty Calibration in Deep Learning' (NeurIPS 2020)

The official repository (in PyTorch) for the ECCV 2020 paper "Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference".

Using graph network to solve PDEs

The implementation of "End-to-End Multi-Task Learning with Attention" [CVPR 2019].

Asymmetric Multi-Task Attention Network for Prostate Bed Segmentation in CT Images

Multilayer Nonnegative Matrix Factorization