Pinned Repositories
adaptedDiffusion
Try to adapt diffusion models in medical images
Air2Fortune
Game "Air to Fortune" in Mario Party of Switch console
airport-codes
List of Airport codes, locations and other information around the world
Alan_pet_psf_gibbs
ALTS
Active Learning in TotalSegmentator
DeepRad_demos
Small demos of processing medical datasets in a deep learning way
friendly-bassoon
Simulation based medical images super resolution
Medical-Image-4-Machine-Learning
Public medical image datasets
MIMRTL_cGAN
Modified from cycleGAN and add ndarray input/output and 3d version
SUREMI
Segmentations as Unified Representations for Medical Images
WeijieChen2017's Repositories
WeijieChen2017/friendly-bassoon
Simulation based medical images super resolution
WeijieChen2017/SUREMI
Segmentations as Unified Representations for Medical Images
WeijieChen2017/adaptedDiffusion
Try to adapt diffusion models in medical images
WeijieChen2017/Alan_pet_psf_gibbs
WeijieChen2017/ALTS
Active Learning in TotalSegmentator
WeijieChen2017/ColdDiffusionMedicalImages
Try to adapt the cold diffusion framework to medical images
WeijieChen2017/Amoonguss
WeijieChen2017/BMI_881_883_Scholarly_Literature_Professional_Skills
Critical evaluation of the scholarly literature is a crucial skill for researchers. Through this course (along with its continuation, BMI 882), Ph.D. students in Biomedical Data Science will develop this valuable skill by focused reading and discussion of a variety of journal articles of present or historical importance the biomedical sciences literature, including biostatistics, biomedical informatics, and relevant topics in statistics and computer science. Students will read and discuss one article or a small group of related articles each week. Students will provide short written summaries in advance of discussion. In addition to the readings and discussion, there will be two written homework assignments related to the articles under discussion. These homework assignments will involve an effort to reproduce the results of an article, the use of computer simulation to investigate properties of methods discussed in an article, or application of the discussed methods to related biomedical data.
WeijieChen2017/ComplexML
A toy example for complex number machine models
WeijieChen2017/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
WeijieChen2017/DuettoSyn
PET/MR transformer
WeijieChen2017/ESIWSAS
This is a private repository.
WeijieChen2017/FourierU-Net
This project is to discuss how skip connections and backbone features
WeijieChen2017/LakeLatios
This is to explore paired image translation using diffusion model network framework
WeijieChen2017/Magnamalo
WeijieChen2017/MedMAE
We use the Masked Autoencoder to train a uniform encoder for medical images, including MR, PET and CT
WeijieChen2017/MIMRTL_PVC
Partial volume correction project in MIMRTL lab
WeijieChen2017/NAC2MAC
NAC PET 2 MAC PET via Unet
WeijieChen2017/pytorch-3dunet
3D U-Net model for volumetric semantic segmentation written in pytorch
WeijieChen2017/Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
WeijieChen2017/pyTorch_imageTranslation
Image translation via pyTorch
WeijieChen2017/SharkSeagrass
"DolphinCoral" is a machine learning project that synthesizes CT images from PET scans using advanced VQ encoder-decoder frameworks and innovative model architectures.
WeijieChen2017/ShinyPalmTree
A project using cycle consistence loss to reconstruct PET images without partial volume effect
WeijieChen2017/SNGL_NAC_CTAC
Single NAC PET based CT attenuation correction
WeijieChen2017/TheSkyOfUS
WeijieChen2017/Transformer_for_medical_image_analysis
A collection of papers about Transformer in the field of medical image analysis.
WeijieChen2017/transformer_in_medical_image
This is a demo for using transformers to translate medical images.
WeijieChen2017/UNETR_NAC2CT
WeijieChen2017/UW-CHTC-HTCondor-Examples
Example scripts for running HTCondor jobs on the University of Wisconsin Center for High Throughput Computing (CHTC) Resources
WeijieChen2017/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch