There are 0 repository under nih-chest-x-ray topic.
A multi-label-classification model for common thorax disease.
"Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray" by Debojyoti Pal, Pailla Balakrishna Reddy, and Sudipta Roy.
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
Implementation of Deep Neural Networks to solve Medical Image Classification using Chest XRay Images
ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification
Comprehensive Performance Analysis of Three Pretrained Transformer Models (ViT, Swin, and MaxViT) on ImageNet and Fine-tuned on the NIH Chest X-rays Dataset for Classifying 14 Chest Radiograph Pathologies
Designed a machine learning model to predict the diseases from the images of chest X-Ray comprising of 14 diseases using novel approaches like mobile net, efficient net and try to build upon it using some new approaches like federated learning and wavelets based techniques.
Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning