chengjianhong

3D-MRI-brain-tumor-segmentation-using-autoencoder-regularization

Pytorch implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. [https://arxiv.org/abs/1810.11654]

BraTS2018-tumor-segmentation

We provide DeepMedic and 3D UNet in pytorch for brain tumore segmentation. We also integrate location information with DeepMedic and 3D UNet by adding additional brain parcellation with original MR images.

dogs_vs_cats

猫狗大战

FedNova

PyTorch implementation of FedNova (NeurIPS 2020), and a class of federated learning algorithms, including FedAvg, FedProx.

glioma_grading

Glioma_IDH

hands-on-transfer-learning-with-python

Deep learning simplified by transferring prior learning using the Python deep learning ecosystem

IDH

IDH_Prediction

Use of Deep Learning to Predict IDH status from MR Imaging

image-segmentation-keras

Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

chengjianhong/glioma_grading

chengjianhong/Glioma_IDH

chengjianhong/SoTA-Brain-Tumor-Segmentation

chengjianhong/3D-CycleGan-Pytorch-MedImaging

Pytorch pipeline for 3D image domain translation using Cycle-Generative-Adversarial-networks, without paired examples.

chengjianhong/AMR-Benchmark

A Unified Implementation of Several Baseline Deep Learning Models for Automatic Modulation Recognition

chengjianhong/ares

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

chengjianhong/Awesome-Federated-Learning

Federated Learning Library: https://fedml.ai

chengjianhong/brain_mask

3D deep convolutional neural network based segmentation and brain masking tool for multi-modal brain MRI

chengjianhong/CoTr

[MICCAI2021] CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

chengjianhong/Covid-19-CXR

chengjianhong/CSUthesis

中南大学研究生学位论文LaTex模版（博士和硕士）

chengjianhong/D2Net

D2Net for brain tumor segmentation with missing modalities

chengjianhong/dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

chengjianhong/fedavg.pytorch

A PyTorch implementation of "Communication-Efficient Learning of Deep Networks from Decentralized Data", AISTATS, 2017

chengjianhong/FedBN

[ICLR'21] FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

chengjianhong/FL-MRCM

chengjianhong/Glioma_OS

chengjianhong/HAD_Net

chengjianhong/lifelines

Survival analysis in Python

chengjianhong/models

Models and examples built with TensorFlow

chengjianhong/MSFN

chengjianhong/Patch-GCN

Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks - MICCAI 2021

chengjianhong/pycox

Survival analysis with PyTorch

chengjianhong/RAST

chengjianhong/research-contributions

Implementations of recent research prototypes/demonstrations using MONAI.

chengjianhong/segtran

Medical Image Segmentation using Squeeze-and-Expansion Transformers

chengjianhong/simclr

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

chengjianhong/SSMLL-CAP

chengjianhong/WSISA

Zhu, Xinliang, et al. "Wsisa: Making survival prediction from whole slide histopathological images." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

chengjianhong/WSISIA_

implement "WSISA: Making Survival Prediction from Whole Slide Histopathological Images"