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Development #9

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Nov 20, 2023
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Development #9

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ccb6bb1
add wiki page on NBIA
Nov 20, 2023
f7474f0
Add documentation status badge
Nov 20, 2023
201dda7
Merge pull request #8 from jjjermiah/main
jjjermiah Nov 20, 2023
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1 change: 1 addition & 0 deletions README.md
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[![PyTests](https://github.com/jjjermiah/NBIA-toolkit/actions/workflows/main.yml/badge.svg)](https://github.com/jjjermiah/NBIA-toolkit/actions/workflows/main.yml)
[![Documentation Status](https://readthedocs.org/projects/nbia-toolkit/badge/?version=latest)](https://nbia-toolkit.readthedocs.io/en/latest/?badge=latest)

# none of this works yet lol but it will soon
# NBIA Toolkit
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:maxdepth: 2
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docs/wiki/NBIA.md
example.ipynb
changelog.md
contributing.md
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# National Biomedical Imaging Archive (NBIA)

The National Biomedical Imaging Archive (NBIA) is a free and open-source platform that provides the biomedical research community with access to biomedical images, annotations, and related data.
- It is developed by the [National Cancer Institue (NCI)](https://www.cancer.gov/) and aims to improve healthcare by fostering research in areas including cancer, lung diseases, and brain disorders.

The NBIA is primarily used in the fields of medical and health research.
- This includes areas such as radiology, oncology, neurology, pathology and more.

1. Radiology: Radiologists use this archive to access a variety of images such as CT scans, MRI scans that help them in the diagnosis and treatment of various diseases.

2. Oncology: Oncologists use it to study various types of cancers. They can access images from different stages of cancer which aids them in understanding the progression of the disease.

3. Neurology: Neurologists use it to study various neurological disorders like Alzheimer’s, Parkinson’s disease etc., by analyzing brain scans available in the archive.

4. Pathology: Pathologists use it for studying disease processes and analyzing tissue samples from patients with various diseases.

5. Clinical Trials: The NBIA is also used extensively in clinical trials where imaging plays a crucial role. Researchers can share their findings with others around the world which promotes collaborative studies.

6. Machine Learning/AI Research: The large collection of biomedical images also serves as a valuable resource for researchers working on developing machine learning algorithms for image recognition and diagnosis purposes.
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