ProfessorHuang

Dive-into-DL-PyTorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

Pytorch-UNet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

MONAI

AI Toolkit for Healthcare Imaging

nnUNet

unet

unet for image segmentation

darts

Differentiable architecture search for convolutional and recurrent networks

handong1587.github.io

agents

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

UNetPlusPlus

[IEEE TMI] Official Implementation for UNet++

MICCAI-OpenSourcePapers

MICCAI 2019-2023 Open Source Papers

batchgenerators

A framework for data augmentation for 2D and 3D image classification and segmentation

MedMNIST

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

pytorch-nested-unet

PyTorch implementation of UNet++ (Nested U-Net).

ModelsGenesis

[MICCAI 2019] [MEDIA 2020] Models Genesis

NumPyCNN

Building Convolutional Neural Networks From Scratch using NumPy

PC-DARTS

PC-DARTS:Partial Channel Connections for Memory-Efficient Differentiable Architecture Search

awesome-nas-papers

Awesome Neural Architecture Search Papers

MyDeepLearning

A deep learning library to provide algs in pure Numpy or Tensorflow.

unet

U-Net Biomedical Image Segmentation

NasUnet

TF-NAS

TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (ECCV2020)

FLARE

Official Repository of MICCAI FLARE Challenges

MARL-for-Anatomical-Landmark-Detection

Tensorflow Implementation of Multiple Landmark Detection using Multi-Agent Reinforcement Learning Paper

FLARE2021-Baseline

[MICCAI 2021 Challenge] The official baseline model for MICCAI Challenge: Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE21)

NAS-SCAM

code of MICCAI 2020 paper "NAS-SCAM: Neural Architecture Search-Based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification"

dtk

deformable toolkit used for deforamable physical simulation

AdwU-Net

[MIDL22] Adaptive depth and width U-Net. Search task-specific optimal depth and width of U-Net by DNAS method.