Pinned Repositories
3D_Liver_Biopsy_Sampling
3D liver needle biopsy image sampling and liver fibrosis quantification
Bead_Image_Depth_Prediction
This project classifies the z-axis position of the bead image based on its image feature. The backbone network is ResNet50
DELINEATE
deep learning method for segregating overlapped liver steatosis droplets
DigitalPathology
This is a repo to share to public community Digital Pathology (DP) web application
LandMark_Registration
This is a registration algorithm by landmark selection and modification.
Liver_Cancer_Segmentation
Viable tumor segmentation in liver cancer histopathology Images
MultiStain_WSI_Registration_CycleGAN
Multistained serial WSI registration with cycleGAN
PCR_Prediction_Serial_WSIs_biomarkers
Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains where spatial attention is produced by biomarkers and tumor cells
RPE_MultiHeadGAN
A semi-supervised deep learning approach, namely MultiHeadGAN, to segment low contrast cells from impaired regions in RPE flatmount images
S4_RPE
Self-Supervised Semantic Segmentation for RPE cells
jkonglab's Repositories
jkonglab/S4_RPE
Self-Supervised Semantic Segmentation for RPE cells
jkonglab/DigitalPathology
This is a repo to share to public community Digital Pathology (DP) web application
jkonglab/PCR_Prediction_Serial_WSIs_biomarkers
Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains where spatial attention is produced by biomarkers and tumor cells
jkonglab/RPE_MultiHeadGAN
A semi-supervised deep learning approach, namely MultiHeadGAN, to segment low contrast cells from impaired regions in RPE flatmount images
jkonglab/Liver_Cancer_Segmentation
Viable tumor segmentation in liver cancer histopathology Images
jkonglab/MultiStain_WSI_Registration_CycleGAN
Multistained serial WSI registration with cycleGAN
jkonglab/3D_Liver_Biopsy_Sampling
3D liver needle biopsy image sampling and liver fibrosis quantification
jkonglab/DELINEATE
deep learning method for segregating overlapped liver steatosis droplets
jkonglab/LandMark_Registration
This is a registration algorithm by landmark selection and modification.
jkonglab/Bead_Image_Depth_Prediction
This project classifies the z-axis position of the bead image based on its image feature. The backbone network is ResNet50
jkonglab/RPE_3D_Eyeball_Estimation
estimate 3D eyeball radius from the corresponding 2D flat-mount microscopy image
jkonglab/Bead_Image_Depth_Regression
use DL method to predict depth of beads in image sequences
jkonglab/Fig2Num
extract numbers from bar charts
jkonglab/Fluorescent_Nuclei_Segregation
This is an implementation of nuclei segmentation method for segregating clumped nuclei in DAPI in Fluorescence images.
jkonglab/Foveat_Cell_Segmentation
jkonglab/graphite
GRAPHical Insect Tracking Environment
jkonglab/GRAPHITE-MEE
jkonglab/Liver_Portal_Tract_Segmentation
In this study, we provide our PyTorch implementation of our MUSA-UNet model. The model presents better performance compared with some SOTA approaches such as UNet, FCN, and DeepLab in the liver portal tract segmentation task.
jkonglab/Object_Tracking_3D
tracking 3D object with fluorescent images
jkonglab/PCR_Prediction_MRI_Molecular_Demographics
PCR vs NonPCR classification using MRI, molecular and demographic data
jkonglab/PCR_Prediction_Serial_WSIs_cells
Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains where attention map is produced from tumor cells of finterest
jkonglab/Steatosis_Segmentation
This study aims to segment liver steatosis areas with a deep learning method