pneumothorax Instance Segmentation Dataset and Pre-Trained Model by Kathmandu University

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Explore Dataset

Here are a few use cases for this project:

Medical Diagnosis: Healthcare professionals can use this model to assist in diagnosing pneumothorax from chest x-ray images, accelerating the fulfillment of medical reports, and improving the efficiency of healthcare services.
Emergency Situations: In emergency departments, this model can be used to quickly identify potential pneumothorax cases, ensuring a rapid response which could potentially save lives.
Telemedicine Services: The "pneumothorax" model could be incorporated into telemedicine platforms, providing preliminary diagnoses based on x-ray images and enabling remote consultations, particularly in rural or underserviced areas.
Medical Training: The model could be used as a teaching tool for medical students or junior doctors to learn to identify pneumothorax in chest x-ray images, supplementing their theoretical knowledge with practical examples.
Screening Programs: The model could be integrated into preventative screening programs, allowing for early detection of pneumothorax in at-risk populations, helping prevent complications and improving patient outcomes.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            pneumothorax-erugs_dataset,
                            title = { pneumothorax Dataset },
                            type = { Open Source Dataset },
                            author = { Kathmandu University },
                            howpublished = { \url{ https://universe.roboflow.com/kathmandu-university-hqa4p/pneumothorax-erugs } },
                            url = { https://universe.roboflow.com/kathmandu-university-hqa4p/pneumothorax-erugs },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-04-29 },
                            }