# C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging

arXiv: https://arxiv.org/abs/2505.18745 · HTML: https://mlsanity.com/paper/2505.18745/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [Optimal whitening and decorrelation](https://mlsanity.com/paper/1512.00809/) (2015)
- [SdAE: Self-distillated Masked Autoencoder](https://mlsanity.com/paper/2208.00449/) (2022)
- [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)
- [Learning Implicitly Recurrent CNNs Through Parameter Sharing](https://mlsanity.com/paper/1902.09701/) (2019)
- [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)
- [ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy](https://mlsanity.com/paper/2411.02572/) (2024)
- [Contextual Vision Transformers for Robust Representation Learning](https://mlsanity.com/paper/2305.19402/) (2023)
- [Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers](https://mlsanity.com/paper/2405.16419/) (2024)
- [Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images](https://mlsanity.com/paper/2406.05308/) (2024)
- [Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning](https://mlsanity.com/paper/2503.09826/) (2025)
- [Single-cell Subcellular Protein Localisation Using Novel Ensembles of Diverse Deep Architectures](https://mlsanity.com/paper/2205.09841/) (2022)

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

- [DC-ViT: Modulating Spatial and Channel Interactions for Multi-Channel Images](https://mlsanity.com/paper/2603.14281/) (2026)
- [Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images](https://mlsanity.com/paper/2609.21629/) (2026)

## Abstract (arXiv)

Immunohistochemical (IHC) images reveal detailed information about structures and functions at the subcellular level. However, unlike natural images, IHC datasets pose challenges for deep learning models due to their inconsistencies in channel count and configuration, stemming from varying staining protocols across laboratories and studies. Existing approaches build channel-adaptive models, which unfortunately fail to support out-of-distribution (OOD) evaluation across IHC datasets and cannot be applied in a true zero-shot setting with mismatched channel counts. To address this, we introduce a structured view of cellular image channels by grouping them into either context or concept, where we treat the context channels as a reference to the concept channels in the image. We leverage this context-concept principle to develop Channel Conditioned Cell Representations (C3R), a framework designed for unified evaluation on in-distribution (ID) and OOD datasets. C3R is a two-fold framework comprising a channel-adaptive encoder architecture and a masked knowledge distillation training strategy, both built around the context-concept principle. We find that C3R outperforms existing benchmarks on both ID and OOD tasks, while a trivial implementation of our core idea also outperforms the channel-adaptive methods reported on the CHAMMI benchmark. Our method opens a new pathway for cross-dataset generalization between IHC datasets, without requiring dataset-specific adaptation or retraining.