Pinned Repositories
BDCN-Fiber_Detect
Fiber detection
brain-tumor
Dataset of brain scans w/ tumor for Kaggle
COVID-CT
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Ding1119.github.io
Web
DirectionalFeature
Learning Directional Feature Maps for Cardiac MRI Segmentation (MICCAI2020)
FanChiMao
Config files for my GitHub profile.
Foveation-Segmentation
PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images
GPT-2-in-Chinese-Web
HOGANN_Marijuana
Mask-RCNN_TB
This is an example of object detection on Micro bacterium tuberculosis using Mask-RCNN
Ding1119's Repositories
Ding1119/GPT-2-in-Chinese-Web
Ding1119/BDCN-Fiber_Detect
Fiber detection
Ding1119/LLMMs_FEMH
Ding1119/Mask-RCNN_TB
This is an example of object detection on Micro bacterium tuberculosis using Mask-RCNN
Ding1119/brain-tumor
Dataset of brain scans w/ tumor for Kaggle
Ding1119/COVID-CT
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Ding1119/Ding1119.github.io
Web
Ding1119/DirectionalFeature
Learning Directional Feature Maps for Cardiac MRI Segmentation (MICCAI2020)
Ding1119/FanChiMao
Config files for my GitHub profile.
Ding1119/Foveation-Segmentation
PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images
Ding1119/HOGANN_Marijuana
Ding1119/ML_practice
Ding1119/MS-CMR_miccai_2019
Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.
Ding1119/opensource.guide
📚 Community guides for open source creators
Ding1119/opical-flow-estimation-with-RAFT
Optical Flow Estimation using RAFT with PyTorch.
Ding1119/opticalflow-autoflow
Ding1119/react
A declarative, efficient, and flexible JavaScript library for building user interfaces.
Ding1119/U-Net-Transformer