LymphoMNIST
Introducing LymphoMNIST, a comprehensive dataset tailored for the nuanced classification of lymphocyte images, encompassing an extensive collection of approximately 80,000 high-resolution 64x64 images. This dataset meticulously categorizes lymphocytes into three primary classes: B cells, T4 cells, and T8 cells, thereby facilitating a focused study on these fundamental immune cell types. Designed with precision, LymphoMNIST stands out as a high-quality, MNIST-like repository of standardized biomedical imagery, purposefully curated to ensure immediate applicability without necessitating extensive background knowledge in biomedical imaging.
LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is not only vast in scale but also rich in detail, thereby supporting a wide array of research endeavors, from fundamental biological studies to advanced computational model development. This dataset is particularly curated to cater to a variety of classification challenges, ranging from straightforward binary classifications to more complex multi-class identifications, thereby offering an invaluable resource for both educational and research-based explorations in the fields of biomedical image analysis, computer vision, and machine learning.
By focusing exclusively on B, T4, and T8 lymphocyte cells at a high resolution, LymphoMNIST enables the detailed study and development of specialized models that can accurately distinguish between these cell types, a critical requirement in both clinical diagnostics and immunological research. Whether for academic purposes, algorithm benchmarking, or enhancing the capabilities of open-source and commercial AutoML tools, LymphoMNIST provides a foundational platform for innovation and advancement in the medical imaging domain.
File structure
LymphoMNIST is organized as follows for easy integration into your projects:
LymphoMNIST/
├── LymphoMNIST/
│ ├── __init__.py
│ ├── LymphoMNIST.py # main class for loading the data
│ └── utils.py # Additional utility functions for easier visualization
├── tests/
│ ├── __init__.py
│ └── test_LymphoMNIST.py # Optional: tests for the package
├── setup.py
├── README.md
└── LICENSE
Getting Started with LymphoMNIST
Get up and running with the LymphoMNIST dataset in a few simple steps. This guide will walk you through installing necessary dependencies, setting up the LymphoMNIST package, and loading the dataset for use in your machine learning models.
Step 1: Install Dependencies
pip install torch torchvision Pillow numpy tqdm requests matplotlib
Step 2: Install LymphoMNIST package
pip install LymphoMNIST
Step 3: Check LymphoMNIST version
import LymphoMNIST as info
print(f"LymphoMNIST v{info.__version__} @ {info.HOMEPAGE}")
For a detailed tutorial on using LymphoMNIST, follow this Google Colab notebook.
Built With
License
Distributed under the Apache License. See LICENSE for more information.
Contact
Khayrul Islam - @LinkedIN - khayrulbuet13@alum.lehigh.edu
Project Link: LymphoMNIST
Acknowledgments
This project is funded by:
Release Note
Initial release: This is the first release of LymphoMNIST, marking the introduction of the dataset to the research community.
Metadata
Release files for LymphoMNIST 1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lymphomnist-1.0.tar.gz | 53.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lymphomnist-1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 71.3 kB
Release files / lymphomnist-1.0.tar.gz
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| Download URL | lymphomnist-1.0-py3-none-any.whl |
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| Tags | Python 3 |
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