This repository contains the Python implementation used in the study "Investigating Osteoporosis Diagnosis Using Clinical and Hip X-Ray Data from the Syrian Population". The notebook implements the complete experimental workflow described in the manuscript, including clinical machine learning models, deep learning classification of hip radiographs, hybrid multimodal classification using clinical and image-derived features, preprocessing, training, evaluation, and reproducibility settings. Users should provide the corresponding datasets in the expected directory structure before execution.
Radiography, renowned for its diagnostic prowess and affordability, plays a key role in detecting diseases, including critical conditions. Chest radiography, focusing on a vital body area, poses interpretational challenges, necessitating experienced radiologists for accurate diag…
Code and data deposit for a two-stage ViT–UNet concrete CT segmentation study. Includes training/evaluation scripts, reproducibility logs (split seed 42), and 151 public test patches (images + masks). Train/val data, 0401 manual labels, and weights excluded. CC BY-NC 4.0.
Coronary artery disease is one of the leading causes of morbidity and mortality worldwide, with X-ray coronary angiography serving as the clinical gold standard for diagnosis and intervention planning. Accurate segmentation of coronary arteries is essential for quantitative analy…
Data and code for a leakage-aware evaluation of machine-learning predictors of orthoflavivirus host range. Contains the full analysis pipeline, DNABERT-2 embeddings, window-level sequence data, results, and figure-generation scripts to reproduce every figure and result. verify_re…
Code and dataset accompanying the manuscript "Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors." Includes the symbolic-regression scripts (PySR) for discovering closed-form mass-attenuation laws of lead-free PEI/me…
Code and data for "Chemical Identity Lost in Regulation: A Study of Semantic Interoperability in European Chemical Substance Data" (Van Haute, Goedertier, Fannes, Van de Wynckel), Poster & Demo track, SEMANTiCS 2026, Ghent. European regulatory datasets represent chemical substanc…