# Bioptimus Documentation

## Docs

- [H-Optimus (H1) tile embedding](https://docs.bioptimus.com/api-reference/embedding/h-optimus-h1-tile-embedding.md): Compute H1 tile embeddings. Input: a 224×224 tile at 0.5 MPP. Output: a 1536-dimensional embedding vector in `output`.
- [M-Optimus tile embedding](https://docs.bioptimus.com/api-reference/embedding/m-optimus-tile-embedding.md): Compute M-Optimus tile embeddings. Input: a 224×224 tile at 0.5 MPP. Output: an embedding vector in `output`.
- [Bioptimus Inference Server](https://docs.bioptimus.com/api-reference/introduction.md): The REST API served by the Bioptimus Model Server, and the SageMaker dispatch endpoint.
- [M-Optimus gene sets](https://docs.bioptimus.com/api-reference/prediction/m-optimus-gene-sets.md): Returns the ordered input and output gene sets used by the M-Optimus model.
- [M-Optimus spatial gene expression prediction](https://docs.bioptimus.com/api-reference/prediction/m-optimus-spatial-gene-expression-prediction.md): Predict spatial gene expression from a tile and optional bulk RNA counts. Input: a 224×224 tile at 0.5 MPP, plus an optional `bulk_rna` vector aligned to the model's input gene set (see GET /api/metadata/m-optimus). When `bulk_rna` is omitted, a zero vector is used (H&E-only mode). Output: predicted…
- [Tissue segmentation](https://docs.bioptimus.com/api-reference/prediction/tissue-segmentation.md): Generate a tissue segmentation mask for a tile. Input: a 512×512 tile at 8.0 MPP. Output: a flattened binary mask (0/1) of length H×W in `output`.
- [Health check](https://docs.bioptimus.com/api-reference/service/health-check.md): Returns 200 with the loaded model list when the server is ready. Returns 503 with a status reason while loading or if the GPU/CUDA context is unavailable.
- [SageMaker dispatch endpoint](https://docs.bioptimus.com/api-reference/service/sagemaker-dispatch-endpoint.md): SageMaker-compatible endpoint. Accepts a SageMakerRequest (ModelRequest plus `model_name` and `mode`) to route to the correct model and mode. Used by the SDK's AWS backend.
- [Service discovery](https://docs.bioptimus.com/api-reference/service/service-discovery.md): Returns links to the interactive docs and the OpenAPI schema.
- [Deployment overview](https://docs.bioptimus.com/deployment/overview.md): Choose the right way to access Bioptimus models.
- [AWS & SageMaker](https://docs.bioptimus.com/deployment/platforms/aws-sagemaker.md): Deploy a Bioptimus model from AWS Marketplace to a SageMaker endpoint.
- [Hugging Face](https://docs.bioptimus.com/deployment/platforms/hugging-face.md): Load H-Optimus-1 weights directly for non-commercial academic research.
- [On-premise](https://docs.bioptimus.com/deployment/platforms/on-premise.md): Load, run, and verify the Bioptimus Model Server container in your own environment.
- [Requirements](https://docs.bioptimus.com/deployment/requirements.md): Prerequisites for each deployment option.
- [Validation & acceptance testing](https://docs.bioptimus.com/deployment/validation.md): Verify a deployment produces correct, expected outputs.
- [Welcome](https://docs.bioptimus.com/documentation/introduction.md): Deploy and build with foundation models for biology — across histology, transcriptomics, and genomics.
- [Benchmarks](https://docs.bioptimus.com/documentation/models/benchmarks.md): Independent benchmark results for Bioptimus pathology models.
- [H-Optimus](https://docs.bioptimus.com/documentation/models/h-optimus.md): A vision foundation model for histology feature extraction.
- [M-Optimus](https://docs.bioptimus.com/documentation/models/m-optimus.md): A multimodal, multi-scale foundation model that predicts spatial gene expression from histology.
- [Tissue segmentation](https://docs.bioptimus.com/documentation/models/tissue-segmentation.md): Separate tissue from background before feature extraction.
- [Quickstart](https://docs.bioptimus.com/documentation/quickstart.md): Pick your model and platform, then run your first inference.
- [FAQ](https://docs.bioptimus.com/documentation/resources/faq.md): Common questions about deploying and using Bioptimus models.
- [Glossary](https://docs.bioptimus.com/documentation/resources/glossary.md): Key terms used across Bioptimus documentation.
- [Responsible use](https://docs.bioptimus.com/documentation/resources/responsible-use.md): Intended use, regulatory status, license terms, responsibilities, and disclaimer.
- [Drug target & biomarker discovery](https://docs.bioptimus.com/documentation/use-cases/biomarker-discovery.md): Predict drug target distribution, immune microenvironment, and stromal architecture from routine histology slides.
- [Diagnostics & spatial biology](https://docs.bioptimus.com/documentation/use-cases/diagnostics-spatial-biology.md): Integrate histology, spatial transcriptomics, and genomics in a single pipeline.
- [Indication expansion](https://docs.bioptimus.com/documentation/use-cases/indication-expansion.md): Identify the patient subsets and indications where a therapeutic is most likely to succeed.
- [Treatment response & trial design](https://docs.bioptimus.com/documentation/use-cases/treatment-response.md): Discover multimodal biomarker signatures that distinguish responders from non-responders.
- [Inference pipeline](https://docs.bioptimus.com/guides/get-started/inference-pipeline.md): Configure the whole pipeline with one object — tissue masking, embeddings, predictions, and reproducible workspaces.
- [Bioptimus SDK](https://docs.bioptimus.com/guides/get-started/sdk.md): The Bioptimus Python SDK — installation and the two ways to use it.
- [Visualizing results](https://docs.bioptimus.com/guides/get-started/visualizing-results.md): Load Zarr/HDF5/NPZ outputs and overlay gene expression or tissue masks.
- [WSI processing](https://docs.bioptimus.com/guides/reference/wsi-processing.md): Read whole-slide images: pyramid levels, MPP, regions, and thumbnails.
- [Cohorts](https://docs.bioptimus.com/guides/workflows/cohort.md): Run a model over a cohort of slides with shared tissue masks and a structured workspace.
- [Tile embeddings & PCA](https://docs.bioptimus.com/guides/workflows/embeddings-pca.md): Extract tile embeddings (H-Optimus or M-Optimus) and visualize morphology with PCA.
- [Spatial transcriptomics](https://docs.bioptimus.com/guides/workflows/spatial-transcriptomics.md): Predict spatial gene expression from an H&E slide with M-Optimus, end to end.
- [License](https://docs.bioptimus.com/license.md)
- [cohort](https://docs.bioptimus.com/sdk-reference/data/cohort.md)
- [multi_model_dataset](https://docs.bioptimus.com/sdk-reference/data/multi-model/multi_model_dataset.md)
- [bioptimus.data.omics.bulkrna](https://docs.bioptimus.com/sdk-reference/data/omics/bulkrna.md)
- [tile_spec](https://docs.bioptimus.com/sdk-reference/data/tile_spec.md)
- [torch_dataset](https://docs.bioptimus.com/sdk-reference/data/wsi/torch_dataset.md)
- [wsi_dataset](https://docs.bioptimus.com/sdk-reference/data/wsi/wsi_dataset.md)
- [megatile_extraction](https://docs.bioptimus.com/sdk-reference/extraction/wsi/megatile_extraction.md)
- [tile_extraction](https://docs.bioptimus.com/sdk-reference/extraction/wsi/tile_extraction.md)
- [types](https://docs.bioptimus.com/sdk-reference/extraction/wsi/types.md)
- [schemas](https://docs.bioptimus.com/sdk-reference/inference/schemas.md)
- [writers](https://docs.bioptimus.com/sdk-reference/inference/writers.md)
- [bioptimus.io.wsi.factory](https://docs.bioptimus.com/sdk-reference/io/wsi/factory.md)
- [bioptimus.io.wsi](https://docs.bioptimus.com/sdk-reference/io/wsi/index.md): WSI (Whole Slide Image) module for reading digital pathology slides.
- [bioptimus.io.wsi.interface](https://docs.bioptimus.com/sdk-reference/io/wsi/interface.md)
- [bioptimus.io.wsi.metadata](https://docs.bioptimus.com/sdk-reference/io/wsi/metadata.md): WSI Metadata Properties Module.
- [types](https://docs.bioptimus.com/sdk-reference/io/wsi/types.md)
- [backbones](https://docs.bioptimus.com/sdk-reference/models/backbones.md)
- [aws](https://docs.bioptimus.com/sdk-reference/models/clients/aws.md)
- [http](https://docs.bioptimus.com/sdk-reference/models/clients/http.md)
- [config_loader](https://docs.bioptimus.com/sdk-reference/models/config_loader.md)
- [endpoint_model](https://docs.bioptimus.com/sdk-reference/models/endpoint_model.md)
- [types](https://docs.bioptimus.com/sdk-reference/models/types.md)
- [base](https://docs.bioptimus.com/sdk-reference/preprocess/omics/base.md)
- [factory](https://docs.bioptimus.com/sdk-reference/preprocess/omics/factory.md)
- [transforms](https://docs.bioptimus.com/sdk-reference/preprocess/omics/transforms.md)
- [bioptimus.preprocess.wsi.models.base](https://docs.bioptimus.com/sdk-reference/preprocess/wsi/models/base.md)
- [bioptimus_mask](https://docs.bioptimus.com/sdk-reference/preprocess/wsi/models/bioptimus_mask.md)
- [bioptimus.preprocess.wsi.provider.base](https://docs.bioptimus.com/sdk-reference/preprocess/wsi/provider/base.md): Abstract interface for tissue mask providers.
- [bioptimus.preprocess.wsi.provider.precomputed](https://docs.bioptimus.com/sdk-reference/preprocess/wsi/provider/precomputed.md): Pre-computed tissue mask provider.
- [bioptimus.preprocess.wsi.provider.tiled](https://docs.bioptimus.com/sdk-reference/preprocess/wsi/provider/tiled.md)

## OpenAPI Specs

- [openapi](https://docs.bioptimus.com/openapi.json)

## Optional

- [Website](https://www.bioptimus.com)
- [GitHub](https://github.com/bioptimus)
- [Knowledge Base](https://www.bioptimus.com/knowledge-base)