Patch-Based Cell Tokenization

• Patch-Based Cell Tokenization is a strategy that decomposes biological data into discrete units to enable machine learning models to capture spatial and functional relationships.
• It employs methods ranging from fixed grid to adaptive and semantic patching for imaging and omics data, optimizing efficiency and interpretability.
• This approach enhances cellular analysis by facilitating scalable, context-aware processing for high-resolution histopathology and multi-omics integration.

Patch-based cell tokenization refers to strategies in which raw biological data, images, or omics profiles are decomposed into discrete, often non-overlapping or adaptively sized "patches" that serve as the fundamental units (“tokens”) for downstream machine learning—typically, transformer-based or graph neural network models. While the approach originated in computer vision (e.g., dividing images into rectangular image patches for Vision Transformers), patch-based tokenization has been generalized and tailored for cellular data, including histopathology whole-slide images, single-cell multi-omics, and even high-throughput transcriptomic atlases.

1. Principles of Patch-Based Cell Tokenization

Patch-based cell tokenization replaces monolithic or pixel-/feature-wise representations with coarse-grained groupings—patches—intended to focus computational and modeling capacity on the most relevant substructures. In microscopy, a “patch” often means a fixed-size square region of the image; in genomics, it can denote a contiguous stretch of the genome (adjacent genes/peaks), and in embeddings of sequencing data, it may represent the entire feature vector for a single cell. The overarching objective is to introduce locality, regularization, or context-awareness, thereby facilitating the capture of spatial or functional relationships within and across cells.

Patch selection can be static (uniform grids; e.g., ViT) or adaptive (e.g., mixed-resolution, subobject, superpixel). The discrete tokens produced by these patching choices are mapped to latent, fixed-length feature vectors for transformer, graph convolutional, or LLM input.

2. Methodologies in Imaging: Grid, Adaptive, and Semantic Patching

Early implementations in histopathology adopted simple fixed-size rectangular patching to break large whole-slide images (WSIs) into smaller regions, with each patch embedded for downstream modeling by CNNs or transformers (2502.02471). Formally, for an image $\mathbf{x} \in \mathbb{R}^{H \times W \times C}$, the division yields $N = HW/P^2$ patches (for patch size $P$), each mapped via: $$\mathbf{z}_i = \mathbf{E} \cdot \text{Flatten}(\mathbf{x}_i), ~~i=1 \ldots N$$

Adaptive and semantic tokenization advances classic patching. Mixed-resolution approaches use a saliency-guided Quadtree, recursively splitting an image more finely where "importance" (e.g., semantic or edge-based) is detected. The algorithm selects patches to split based on a score function (e.g., neural feature loss, pixel-level MSE), minimizing resource allocation to background: $$p_{split} = \arg\max_{p \in P_{splittable}} \mathrm{score}(p)$$ Such "patch mosaics" can dramatically reduce token count, focusing computational effort on biologically salient structures (e.g., targeted at cells) and improving model performance per FLOP (2304.00287).

Superpixel and subobject tokenization further departs from the rigidity of grids by aligning tokens with perceptually or graph-based over-segmentations (e.g., SLIC, SPiT, HOOK). Tokens correspond to semantically independent regions, ensuring that each token more often represents a single biological entity or portion thereof (2412.04680, 2408.07680, 2403.18593). In such frameworks, features are aggregated over variable-shaped regions and concatenated with scale- and shape-invariant positional encodings.

For biological images, subobject-level tokenization segments actual cells (or cellular substructures) and encodes them as tokens. For each detected region $\mathcal{C}_k$, the embedding is summarized as: $$\mathbf{z}^{(k)} = \langle \mathbf{z}^{(k)}_{\text{avg}}, \mathbf{z}^{(k)}_{\text{max}} \rangle$$ where average and max poolings are performed over the pre-aggregate features of pixels within that region (2412.04680).

3. Patch-Based Cell Tokenization Beyond Imaging: Omics and Sequencing

In single-cell multi-omics, patch-based tokenization generalizes to the domain of sequencing reads or chromatin accessibility profiles. Instead of selecting a subset of highly variable genes, the entire set of genes or peaks is first ordered according to genomic coordinates and then partitioned into patches—genomic regions or local pools of features. Each patch serves as a token, and a cell is represented as a "sentence" (sequence of patches) (2506.20697).

Given a cell’s raw profile $x \in \mathbb{R}^L$, it is reshaped: $x_p \in \mathbb{R}^{C \times P}$ with $C$ patches, each of size $P$, and embedded as: $T = [t^{(1)}W; \ldots; t^{(C)}W] + E_{pos}$ This approach ensures maximal retention of positional and context information, avoiding the information loss typical of highly variable gene selection.

4. Graph-Based and Hierarchical Tokenization in Cell Analysis

Hierarchical models, exemplified by C2P-GCN, expand upon patch-based tokenization by constructing two-level graphs. At the first stage, cells detected within a patch (e.g., via a nuclei detector) are nodes in a patch-level spatial graph; at the second, each patch (capturing local cell organization) is a node in an image-level graph linked by feature similarity (2403.04962). Adjacency matrices and derived features—the average degree, clustering, distances—summarize spatial structure, while global relationships across patches are expressed through a GCN: $$H^{l+1} = \mathrm{Dropout}(\mathrm{ReLU}(\mathrm{GCN}_l(X^l, A'_I; W^l)))$$ This hierarchical patch tokenization enables efficient, structure-preserving analysis and supports robust performance with far fewer training samples.

5. Evaluation and Benchmarking Metrics

Patch-based cell tokenization is assessed via accuracy (for classification), intersection-over-union (IoU), panoptic quality (PQ), clustering metrics (ARI/NMI), and computational efficiency. In downstream histopathology and brain datasets, hybrid CNN-transformer models leveraging multi-level patch embeddings outperform both generalist and tissue-specific ViTs for cell instance segmentation and classification (2502.02471). In multi-omics integration, patch-based cell tokenization combined with contrastive learning achieves state-of-the-art alignment and cell matching accuracy (2506.20697).

Benchmarks show that adaptive and semantic tokenization (e.g., HOOK, superpixel, subobject) yield superior accuracy, efficiency (token reduction, faster inference), and interpretability compared to grid-based patching. For large images and high-dimensional omics, patch-based approaches enable tractable scaling and robust downstream clustering, trajectory inference, and cell type annotation.

6. Advantages, Limitations, and Biological Implications

Advantages: Patch-based tokenization maintains or improves accuracy for a given compute, enables semantically or positionally aligned tokenization, and allows model resources to be dynamically routed toward relevant biological units such as cells or genes. It naturally supports modularity—tokenization is largely decoupled from network architecture—and interpretability, since the meaning of each token is increasingly tied to a real-world biological entity or mechanism.

Limitations: Challenges include additional preprocessing steps (e.g., segmentation/model-based region detection), dependence on accurate priors (e.g., saliency, segmentation, nuclei location), and potential for context loss in edge cases (e.g., highly overlapping cells, sparse or noisy data). For biomedical applications, segmentation quality and domain adaptation of tokenization algorithms remain active areas of research.

A plausible implication is that as biological datasets grow in size and complexity, patch-based tokenization—adaptive and context-aware in particular—will be essential for scaling foundation models, integrating multi-modal data, and making biological inferences both efficient and interpretable.

7. Comparative Table: Patch-Based Versus Adaptive Tokenization Strategies

Aspect	Fixed Grid Patches	Adaptive Patches (Quadtree, Superpixel, Subobject)
Token Count	Constant, patch size only	Variable, task- or context-driven
Semantic Alignment	Rare	Typical (matches objects/cells/regions)
Efficiency (Tokens/Speed)	Lower	Higher (fewer tokens, less redundancy)
Interpretability	Limited	Enhanced
Scaling	Bottlenecks at high-res	Tractable, especially for large images or omics

8. Broader Outlook

Patch-based cell tokenization practices are now foundational for state-of-the-art methods in vision, omics integration, and time series analysis. Techniques vary from naïve grid partitioning to advanced, context-aware adaptive methods, but the shared goal is to produce tokens—at the level of cells, organelles, genomic loci, or spatiotemporal patches—that best capture the functional or structural units relevant to the problem domain. As evidence accumulates, these strategies are likely to remain central in both algorithmic development and biological discovery, where structure-aware, scalable, and interpretable representations are paramount.