# CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

arXiv: https://arxiv.org/abs/2310.19224 · HTML: https://mlsanity.com/paper/2310.19224/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 77 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [SWAD: Domain Generalization by Seeking Flat Minima](https://mlsanity.com/paper/2102.08604/) (2021)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [All in One: Exploring Unified Video-Language Pre-training](https://mlsanity.com/paper/2203.07303/) (2022)
- [Domain Generalization by Mutual-Information Regularization with Pre-trained Models](https://mlsanity.com/paper/2203.10789/) (2022)
- [ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis](https://mlsanity.com/paper/2004.01113/) (2020)
- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)
- [Harnessing Out-Of-Distribution Examples via Augmenting Content and Style](https://mlsanity.com/paper/2207.03162/) (2022)
- [Learning Implicitly Recurrent CNNs Through Parameter Sharing](https://mlsanity.com/paper/1902.09701/) (2019)
- [Neural Parameter Allocation Search](https://mlsanity.com/paper/2006.10598/) (2020)

## Built on by (most cited first, 18 of 18)

- [Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers](https://mlsanity.com/paper/2405.16419/) (2024)
- [ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning](https://mlsanity.com/paper/2503.19331/) (2025)
- [Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning](https://mlsanity.com/paper/2503.09826/) (2025)
- [Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models](https://mlsanity.com/paper/2505.11482/) (2025)
- [Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization](https://mlsanity.com/paper/2504.02996/) (2025)
- [Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling](https://mlsanity.com/paper/2504.09544/) (2025)
- [C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging](https://mlsanity.com/paper/2505.18745/) (2025)
- [CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images](https://mlsanity.com/paper/2512.20833/) (2025)
- [Generalization of Self-Supervised Vision Transformers for Protein Localization Across Microscopy Domains](https://mlsanity.com/paper/2602.05527/) (2026)
- [DC-ViT: Modulating Spatial and Channel Interactions for Multi-Channel Images](https://mlsanity.com/paper/2603.14281/) (2026)
- [Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization](https://mlsanity.com/paper/2603.16662/) (2026)
- [JUMP-lite: Compact, reproducible benchmarking of cell representations](https://mlsanity.com/paper/2608.07632/) (2026)
- [A Unified Framework for Connecting Noise Modeling to Boost Noise Detection](https://mlsanity.com/paper/2312.00827/) (2023)
- [Channel-Aware Probing for Multi-Channel Imaging](https://mlsanity.com/paper/2602.12696/) (2026)
- [Deep Learning for BioImaging: What Are We Really Learning?](https://mlsanity.com/paper/2603.13377/) (2026)
- [Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization](https://mlsanity.com/paper/2604.10970/) (2026)
- [MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays](https://mlsanity.com/paper/2605.15383/) (2026)
- [Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images](https://mlsanity.com/paper/2609.21629/) (2026)

## Abstract (arXiv)

Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on instruments and experimental goals. Yet, there has not been a systemic attempt to create and evaluate neural networks that are invariant to the number and type of channels. As a result, trained models remain specific to individual studies and are hardly reusable for other microscopy settings. In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework. In addition, we adapted several existing techniques to create channel-adaptive models and compared their performance on this benchmark to fixed-channel, baseline models. We find that channel-adaptive models can generalize better to out-of-domain tasks and can be computationally efficient. We contribute a curated dataset (https://doi.org/10.5281/zenodo.7988357) and an evaluation API (https://github.com/broadinstitute/MorphEm.git) to facilitate objective comparisons in future research and applications.