The processed data of bright-field images of hematopoietic tumor cell lines for machine learning

Morphological images of cells contain extensive information, which help biologists to infer the type and state of cells to some degree based on their morphology. Convolutional Neural Network (CNN), a neural network architecture, is a powerful tool used for image recognition. However, whether it can be used to classify cells on the basis of their morphology remains unclear.<br>We have obtained bright-field images of 10 human hematopoietic tumor-derived cell lines (including acute myeloid leukemia, chronic myeloid leukemia, B-cell acute lymphoblastic leukemia, and myeloma) by imaging flow cytometer (90,000 images per group). These data have been processed for use in Keras. Thus, we believe that these data could be useful for research in the field of cell biology using machine learning.<br><br>