12000+ manually drawn pixel-level lung segmentations, with and without covid

ICVGIP' 18 Oral Paper - Classification of thoracic diseases on ChestX-Ray14 dataset

[DALI 2022] "Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study" by Gregory Holste, Song Wang, Ziyu Jiang, Thomas C. Shen, Ronald M. Summers, Yifan Peng, and Zhangyang Wang

Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation

Bone suppression in chest X-rays: A deep survey. ℱℯℯ𝓁 𝒻𝓇ℯℯ to contribute!

[MLHC 2020] Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts (Jabbour, Fouhey, Kazerooni, Sjoding, Wiens). https://arxiv.org/abs/2009.10132

Detecting Pneumonia from Chest X-ray using a Convolutional Neural Network

AI application on Django 2 - "MedRadService"

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.

Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'

Repository for the 'best student paper award' winning paper at the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022), Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography, Mahbub Ul Alam, Jón Rúnar Baldvinsson and Yuxia Wang. https://doi.org/10.11…

Pediatric CXR radiology impression ML (NLP features + Random Forest + SHAP)

A project About Covid-19 Detection Using Various Deep Learning Algo.

MedGemma Clinical Copilot: Agentic Chest X-Ray Triage System using Google MedGemma 1.5. Features multi-stage safety gating, Grad-CAM explainability, and longitudinal analysis for automated radiology prioritization.

Comparative analysis of 19 Deep Learning architectures (13 SOTA + 6 Proposed) for thoracic disease classification on VinDr-CXR and VinDr-PCXR datasets.

Classical vision method for lung localization in chest X‑rays without deep learning.

AI-powered system to detect Pneumonia from chest X-ray images using a trained deep learning model. Built with FastAPI and TensorFlow

Deep learning-based pneumonia detection from chest X-ray images using DenseNet.