radiographs

There are 0 repository under radiographs topic.

Oulu-IMEDS / DeepKnee
Codes for paper: Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
medical deep-learning pytorch knee biomedical-engineering osteoarthritis radiology radiographs attention-maps
Language:Python 63
xrays-and-gradcam
priyavrat-misra / xrays-and-gradcam
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
fine-tuning vgg16 resnet18 densenet121 gradcam pytorch-implementation covid-19 xrays transfer-learning imbalanced-data oversampling radiographs pneumonia deep-learning cnn early-stopping localization
Language:Jupyter Notebook 46
yongsongH / AIDSRGAN-MICCAI2022
Official Pytorch Code for "Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN" - MICCAI 2022 Workshop
medical-imaging musculoskeletal-radiographs python pytorch radiographs super-resolution
Language:Python 13
Oulu-IMEDS / KneeLocalizer
Codes for paper: A novel method for automatic localization of joint area on knee plain radiographs by A. Tiulpin et. al.
computer-vision detection knee medicine osteoarthritis radiographs
Language:Python 11
simurgailab / an-enhanced-tooth-segmentation-in-bitewing-radiographs
This study was published in 2022 in a scientific journal with SCI-Expanded index. The tooth numbering module uses the FDI notation, which is widely used by dentists, to classify and number dental items found as a result of segmentation. The performance of the Mask R–CNN method used has been proven by comparing it with other state-of-the-art methods.
radiographs tooth bitewing dentistry engineering fdi mask-rcnn segmentation-based-detection classification keras-neural-networks rcnn-model tensorflow
11
manwaarkhd / aariz-cephalometric-dataset
A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification
computer-vision cephalometry classification deep-learning detection orthodontics radiographs x-rays aariz
Language:Python 10
healthcAIr / NNCPR
Practical Applications of Deep Learning: Classifying the most common categories of plain radiographs in a PACS using a neural network (NNCPR)
classification network neural nncpr pacs radiographs
Language:Python 3
manwaarkhd / CEPHA29
CEPHA29 Automatic Cephalometric Landmark Detection Challenge
classification detection orthodontics radiographs cephalometry x-rays computer-vision deep-learning cepha29
Language:Python 3

radiographs

Oulu-IMEDS / DeepKnee

priyavrat-misra / xrays-and-gradcam

yongsongH / AIDSRGAN-MICCAI2022

Oulu-IMEDS / KneeLocalizer

simurgailab / an-enhanced-tooth-segmentation-in-bitewing-radiographs

manwaarkhd / aariz-cephalometric-dataset

healthcAIr / NNCPR

manwaarkhd / CEPHA29