DeepSpot-M

Overview

DeepSpot-M is a multimodal foundation model that maps a 224x224 H&E histology tile to spatial gene expression in log1p-CPM. The output is virtual spatial transcriptomics: one value per queried gene per tile, laid out on the grid the tiles came from.

A LoRA-adapted pathology foundation backbone (Midnight) tokenises the tile. A cross-attention gene decoder lets each gene query attend to the patch tokens, and a gene router hypernetwork builds gene-specific projections from frozen biological embeddings (Evo 2, Orthrus, ProtT5, scGPT, Apertus). Genes enter the model as queryable embeddings rather than fixed output slots, so the released model covers a ~19k protein-coding gene panel including genes unseen in training. The panel ships with the weights as tokens.csv and is exposed as model.gene_names; genes outside it cannot be queried in this release.

Applied to TCGA, the model produced a virtual spatial transcriptomics atlas of 28,664 slides across 32 cancer types.

Licensing

The code is PolyForm Noncommercial 1.0.0 and the weights are CC-BY-NC-SA-4.0. The current model access form additionally restricts eligibility to academic/public nonprofit researchers without concurrent commercial affiliations and excludes commercially funded/collaborative use. Read the live declarations before requesting access; a noncommercial intention alone does not establish eligibility. Research use only, not clinical or diagnostic use. Preserve the applicable attribution and licenses.

Installation

uv pip install deepspotm==1.0.0

PyPI 1.0.0 declares Python >=3.10 without an upper bound; that is not proof of compatibility with every future dependency release. Use an isolated environment. Install the PyTorch build that matches your CUDA runtime if using a GPU. The optional histolab 0.7.0 tiler requires Python <3.12 and an older scientific stack: run tiling separately and exchange PNG tiles plus a coordinate manifest.

Model access