Lung and Colon Cancer Histopathological Image Dataset (LC25000)
By MD Andrew A. Borkowski, M. P. Marilyn M. Bui, MD L. Brannon Thomas, MT1 Catherine P. Wilson, RN1 Lauren A. DeLand, MD1 Stephen M. Mastorides. arXiv.org, 2019
356 citations recorded by the source corpus.
The field of Machine Learning, a subset of Artificial Intelligence, has led to remarkable advancements in many areas, including medicine. Machine Learning algorithms require large datasets to train computer models successfully. Although there are medical image datasets available, more image datasets are needed from a variety of medical entities, especially cancer pathology. Even more scarce are ML-ready image datasets. To address this need, we created an image dataset (LC25000) with 25,000 color images in 5 classes. Each class contains 5,000 images of the following histologic entities: colon adenocarcinoma, benign colonic tissue, lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. All images are de-identified, HIPAA compliant, validated, and freely available for download to AI researchers.
Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/6c5a72aaa3c29d52d46ef904f15719478b6cdfc2
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In Artificial Intelligence
Citations in the corpus
- A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework (2021, methodology): “Borkowski and his associates for constructing the LC25000 dataset and making it available for everyone, which allowed us to use the histopathological images to train and validate the proposed cancer diagnostic model [36,37].”