{"url":"/dataset/lymphomnist","name":"LymphoMNIST","full_name":"LymphoMNIST","description_markdown":"LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images. It encompasses approximately 80,000 high-resolution 64x64 images, meticulously categorized into three primary classes: B cells, T4 cells, and T8 cells.​\r\n\r\nDataset Characteristics:\r\n\r\nSize: ~80,000 images​\r\nResolution: 64x64 pixels​\r\nClasses: B cells, T4 cells, T8 cells​\r\nFormat: MNIST-like standardized biomedical imagery​\r\nModality: Microscopy-based high-resolution cell images​\r\n\r\nMotivation and Summary: LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is vast in scale and rich in detail. It supports a wide array of research endeavors, from fundamental biological studies to advanced computational model development.​\r\n\r\nPotential Use Cases:\r\n\r\nMedical Research: Studying lymphocyte morphology and characteristics​\r\nMachine Learning & AI: Developing and evaluating image classification models​\r\nAutoML & Benchmarking: Serving as a benchmark dataset for automated model training and performance evaluation​\r\nEducational Purposes: Teaching deep learning concepts in biomedical imaging​\r\nData Collection Process: The dataset comprises high-resolution images of lymphocytes obtained through microscopy. Each image is standardized to a 64x64 pixel resolution to maintain consistency and facilitate analysis.​\r\n\r\nAnnotations and Labels: Each image is labeled as one of the three lymphocyte classes: B cells, T4 cells, or T8 cells. The labeling process was conducted by experts in the field to ensure accuracy.","description_withheld":null,"homepage":"https://github.com/Khayrulbuet13/LymphoMNIST","introduced_date":"2025-03-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/real-time-cell-sorting-with-scalable-in-situ","title":"Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning","first_author":"Khayrul Islam","url":null},"license":{"name":"Apache License 2.0","url":"https://github.com/Khayrulbuet13/LymphoMNIST/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Fine-Grained Image Classification","url":"/task/fine-grained-image-classification","datasets_with_task":"/datasets/task/fine-grained-image-classification"},{"name":"Medical Image Classification","url":"/task/medical-image-classification","datasets_with_task":"/datasets/task/medical-image-classification"},{"name":"Semi-supervised Medical Image Classification","url":"/task/semi-supervised-medical-image-classification","datasets_with_task":"/datasets/task/semi-supervised-medical-image-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LymphoMNIST"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}