deepspot-m

# 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. Use it for noncommercial research and check both licences before redistributing outputs. ## Installation ```bash uv pip install deepspotm==1.0.0 ``` Version 1.0.0 targets Python 3.10 to 3.13 and pulls in PyTorch. Install the PyTorch build that matches your CUDA version first if you want GPU inference. ## Model access The weights are gated: 1. Open <https://huggingface.co/ratschlab/DeepSpotM>...