A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

By Andrea Acevedo, A. Merino, Santiago Alférez, Á. Molina, L. Boldú, José Rodellar. Data in Brief, 2020

351 citations recorded by the source corpus.

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection. This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f3ebae8aea7cd5c455fef3d53f785fc627128641

How researchers see this paper

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In Artificial Intelligence

1. Shahriar Baradaran Shokouhi
2. Ahmad Ayatollahi
3. Omid Nejati Manzari
4. Hamid Ahmadabadi
5. Hossein Kashiani

In Computer Vision and Pattern Recognition

1. Shahriar Baradaran Shokouhi
2. Ahmad Ayatollahi
3. Omid Nejati Manzari
4. Hamid Ahmadabadi
5. Hossein Kashiani

Citations in the corpus

• MedViT: A Robust Vision Transformer for Generalized Medical Image Classification (2023, methodology): “BloodMNIST is adapted from a prior blood collection [71].”