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
[ESWA'26] 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
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.
AI application on Django 2 - "MedRadService"
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…
Classifies grayscale chest X-rays as pneumonia or normal with four PyTorch CNNs, FastAPI endpoints, and Docker deployment.
Trains four PyTorch CNN architectures to classify grayscale chest X-rays as pneumonia or normal and serves predictions through FastAPI.
A project About Covid-19 Detection Using Various Deep Learning Algo.
An AI-powered diagnostic system to classify Congenital Heart Diseases (CHD) from chest X-ray images using InceptionV3-classical ensemble stacking.
Pediatric CXR radiology impression ML (NLP features + Random Forest + SHAP)
Comparative analysis of 19 Deep Learning architectures (13 SOTA + 6 Proposed) for thoracic disease classification on VinDr-CXR and VinDr-PCXR datasets.
Classifies grayscale chest X-rays as pneumonia or normal with transfer learning, class balancing, early stopping, and MLflow tracking.
AI-powered system to detect Pneumonia from chest X-ray images using a trained deep learning model. Built with FastAPI and TensorFlow
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