{"categories":[{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"}],"enrichment":{"capability":"TorchXRayVision provides pre-trained deep learning models and unified dataset interfaces for chest X-ray analysis, enabling rapid classification and feature extraction on medical imaging data.","skillfed_tags":["medical-imaging","deep-learning","radiology"],"use_cases":["Rapidly screen large cohorts of chest X-rays for pathology detection using pre-trained models without training from scratch","Extract learned features from X-ray images for downstream machine learning tasks or few-shot learning scenarios","Evaluate and compare new algorithms across multiple public chest X-ray datasets in a standardized way","Build anatomical segmentation pipelines to identify specific chest structures for clinical analysis","Prototype medical imaging research without managing dataset preprocessing and model weight distribution"],"what_it_does":"TorchXRayVision is a PyTorch-based library for working with chest X-ray datasets and pre-trained deep learning models. It addresses two core problems in medical imaging research: avoiding redundant model training by providing models trained on large clinical cohorts, and standardizing dataset access so researchers can swap between multiple public chest X-ray datasets with a single line of code. The library includes DenseNet and ResNet models trained on datasets like NIH ChestX-ray8, CheXpert, MIMIC-CXR, and others, capable of detecting pathologies such as pneumonia, atelectasis, consolidation, and fractures.\n\nThe package wraps torch, torchvision, scikit-image, and image I/O libraries to provide dataset loaders, image preprocessing (normalization, cropping, resizing), and model inference. It also includes autoencoders for representation learning and segmentation models for anatomical landmark detection. Researchers can use pre-trained weights as baselines for rapid analysis or as feature extractors for few-shot learning tasks.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries permissive licensing, and directly solves a real problem in medical imaging research\u2014standardizing access to multiple chest X-ray datasets and providing production-ready pre-trained models. It is well-suited for researchers and practitioners working with chest radiographs who want to avoid redundant model training and dataset engineering."},"id":"torchxrayvision","links":{"html":"https://skillfed.io/packages/torchxrayvision","md":"https://skillfed.io/packages/torchxrayvision.md","pypi":"https://pypi.org/project/torchxrayvision/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-28","license_spdx":null,"license_treatment":"permissive","name":"torchxrayvision","python_support":"supports_current","summary":"TorchXRayVision: A library of chest X-ray datasets and models"},"popularity":{"monthly_downloads":78708,"position":14417,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.2"}