# Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network

arXiv: https://arxiv.org/abs/2509.19896 · HTML: https://mlsanity.com/paper/2509.19896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Masked Siamese Networks for Label-Efficient Learning](https://mlsanity.com/paper/2204.07141/) (2022)
- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://mlsanity.com/paper/2404.10242/) (2024)
- [Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words](https://mlsanity.com/paper/2309.16108/) (2023)
- [Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders](https://mlsanity.com/paper/2403.17823/) (2024)
- [ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy](https://mlsanity.com/paper/2411.02572/) (2024)
- [RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy](https://mlsanity.com/paper/2503.20158/) (2025)
- [Learning Molecular Representation in a Cell](https://mlsanity.com/paper/2406.12056/) (2024)
- [Virtual Cells: Predict, Explain, Discover](https://mlsanity.com/paper/2505.14613/) (2025)
- [CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning](https://mlsanity.com/paper/2506.06290/) (2025)
- [MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction](https://mlsanity.com/paper/2406.08649/) (2024)
- [Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling](https://mlsanity.com/paper/2504.09544/) (2025)

## Built on by (most cited first, 2 of 2)

- [JUMP-lite: Compact, reproducible benchmarking of cell representations](https://mlsanity.com/paper/2608.07632/) (2026)
- [MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images](https://mlsanity.com/paper/2608.22690/) (2026)

## Abstract (arXiv)

Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reducing costly wet-lab iteration. However, extracting biologically meaningful and batch-robust cell painting representations remains challenging. Conventional self-supervised and contrastive learning approaches often require a large-scale model and/or a huge amount of carefully curated data, still struggling with batch effects. We present Cross-Well Aligned Masked Siamese Network (CWA-MSN), a novel representation learning framework that aligns embeddings of cells subjected to the same perturbation across different wells, enforcing semantic consistency despite batch effects. Integrated into a masked siamese architecture, this alignment yields features that capture fine-grained morphology while remaining data- and parameter-efficient. For instance, in a gene-gene relationship retrieval benchmark, CWA-MSN outperforms the state-of-the-art publicly available self-supervised (OpenPhenom) and contrastive learning (CellCLIP) methods, improving the benchmark scores by +29% and +9%, respectively, while training on substantially fewer data (e.g., 0.2M images for CWA-MSN vs. 2.2M images for OpenPhenom) or smaller model size (e.g., 22M parameters for CWA-MSN vs. 1.48B parameters for CellCLIP). Extensive experiments demonstrate that CWA-MSN is a simple and effective way to learn cell image representation, enabling efficient phenotype modeling even under limited data and parameter budgets.