QueeneTam's starred repositories
mri_acq_recon_tutorial
Code for ISBI'19 Tutorial
deep-cine-cardiac-mri
Exploiting temporal redundancies of multi-coil cine cardiac data for MRI reconstruction with unrolled cross-domain networks.
CS_MoCo_LAB
Compressed Sensing and Motion Correction LAB: An MR acquisition and reconstruction system
Motion-Correction-for-pCMR-1
Deep learning based motion correction for cropped perfusion cardiac MR images
retroMoCoBox
Retrospective motion-correction of 3D MRI k-space data in Matlab
FreeFormDeformation-SketchDetection
Real-time image warping based on 2D cubic B-spline free-form deformations, and sketch generation based on edge detection filters
Joint-Motion-Estimation-and-Segmentation
[MICCAI'18] Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Invertible-ISP
[CVPR2021] Invertible Image Signal Processing
Poisson2Sparse
Official Implementation of Poisson2Sparse - MICCAI 2022
DL-ECG-Review
A Review of Deep Learning Methods on ECG Data
fast-soft-sort
Fast Differentiable Sorting and Ranking
ecg-selfsupervised
Self-supervised representation learning from 12-lead ECG data
ecg-diagnosis
Deep learning for 12-lead ECG interpretation
AF-classification
The repo is for the Heart Disease classification project using Transformer Encoders in PyTorch.
longitudinal_autoencoder
Code for the paper Longitudinal self-supervision to disentangleinter-patient variability from disease progression
once-for-all
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
Radon-Cumulative-Distribution-Transform
Radon Cumulative Distribution Transform
qrs_detector
Python Online and Offline ECG QRS Detector based on the Pan-Tomkins algorithm
image-time-series
Code for deep learning-based glioma/tumor growth models
micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape