sqbqamar
Postdoctoral Fellow at Umea University, Sweden
KTH Royal Institute of TechnologyStockholm, Swden
Pinned Repositories
3D_Hyperdense_Seg
3D_UNet
Cascaded-FCN
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
Image-Segmentation
iSeg-2019
Implementation of iSeg-2019
MedicalImaging
Work about Medical Image Segmentation
Spore-Segmentation
Combined approach of CNN and Random Forest
Variant-3D-UNet-for-Infant-Brain
An varient form of 3D_UNet architecture for Infant Brain Segmentation
Yolov8-on-custom-data
YOLOv8 is the latest family of YOLO based Object Detection models from Ultralytics providing state-of-the-art performance. It providing a unified framework for training models for performing Object Detection, Instance Segmentation, and Image Classification.
sqbqamar's Repositories
sqbqamar/Variant-3D-UNet-for-Infant-Brain
An varient form of 3D_UNet architecture for Infant Brain Segmentation
sqbqamar/3D_Hyperdense_Seg
sqbqamar/3D_UNet
sqbqamar/Cascaded-FCN
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
sqbqamar/Image-Segmentation
sqbqamar/iSeg-2019
Implementation of iSeg-2019
sqbqamar/MedicalImaging
Work about Medical Image Segmentation
sqbqamar/Spore-Segmentation
Combined approach of CNN and Random Forest
sqbqamar/Yolov8-on-custom-data
YOLOv8 is the latest family of YOLO based Object Detection models from Ultralytics providing state-of-the-art performance. It providing a unified framework for training models for performing Object Detection, Instance Segmentation, and Image Classification.