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

arXiv: https://arxiv.org/abs/2111.11646 · HTML: https://mlsanity.com/paper/2111.11646/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers](https://mlsanity.com/paper/1906.07282/) (2019)

## Built on by (most cited first, 7 of 7)

- [CHAMMI: A benchmark for channel-adaptive models in microscopy imaging](https://mlsanity.com/paper/2310.19224/) (2023)
- [ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images](https://mlsanity.com/paper/2311.15264/) (2023)
- [Multi-omics Prediction from High-content Cellular Imaging with Deep Learning](https://mlsanity.com/paper/2306.09391/) (2023)
- [Morphological Profiling for Drug Discovery in the Era of Deep Learning](https://mlsanity.com/paper/2312.07899/) (2023)
- [Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation](https://mlsanity.com/paper/2508.19068/) (2025)
- [CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images](https://mlsanity.com/paper/2512.20833/) (2025)
- [Physics-Informed Graph Neural Networks for Frequency-Aware Optical Aberration Correction](https://mlsanity.com/paper/2512.05683/) (2025)

## Abstract (arXiv)

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.