# CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning

Source: [CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning](<https://www.sota2.com/research/paper/68fa5f1c1e209131ce9e7999>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:08:52.839Z

Authors: Stanley Bryan Z. Hua, Alex X. Lu, Alan M. Moses

Published: 2021-11-23T04:37:15.000Z

## Abstract

Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images. Taking inspiration from the success of ImageNet, we curate CytoImageNet, a large-scale dataset of openly-sourced and weakly-labeled microscopy images (890K images, 894 classes). Pretraining on CytoImageNet yields features that are competitive to ImageNet features on downstream microscopy classification tasks. We show evidence that CytoImageNet features capture information not available in ImageNet-trained features. The dataset is made available at https://www.kaggle.com/stanleyhua/cytoimagenet.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.11646>)

[PDF](<https://arxiv.org/pdf/2111.11646.pdf>)