A multi-label-classification model for common thorax disease.

Weakly supervised Classification and Localization of Chest X-ray images

Implementation of Deep Neural Networks to solve Medical Image Classification using Chest XRay Images

"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

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.

A deep learning-powered system for detecting 14 chest diseases from X-ray images using ResNet50, Grad-CAM visualizations, and the NIH Chest X-ray dataset. Designed for interpretability and high diagnostic accuracy, even with class imbalance — using LSE pooling and Binary Cross Entropy loss.

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

Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning

Official implementation of MLVICX, a novel self-supervised learning approach for chest X-ray representation learning. This method captures rich embeddings through multi-level variance and covariance exploration, preserving both fine-grained details and broader contextual information.

ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification