Products & interfaces
| Term | Meaning |
|---|---|
SDK (bioptimus) | The Python client package. Handles WSI reading, tiling, tissue masking, bulk RNA alignment, and concurrent dispatch, against either the on-premise server or a SageMaker endpoint. See the SDK guide. |
Inference (facade) | The high-level, one-object pipeline (config + tissue()/embed()/run()/save()), with cached masks, a structured workspace, and reproducible config. See the Inference facade guide. |
SlideInference | The lower-level, per-slide inference class (Backbone + mask provider + writer) for explicit control. |
Cohort | A batch manifest of slides (and optional bulk RNA) that is the single source of truth for an experiment. Supports late-binding bulk RNA. |
AWSClient | The low-level per-tile client for a SageMaker endpoint; injects model_name/mode into each request. |
OutputFormat | The output writer format — Zarr (default), HDF5, or NPZ. |
| Model Server / API | The FastAPI inference server (shipped in the on-premise container) that exposes the REST endpoints (/api/embed/h1, /api/predict/m-optimus, …) and a Swagger UI at /docs. See the API reference. |
| Backbone | The SDK factory class used to obtain a model client, e.g. Backbone(Models.H1, backend="remote", base_url=...) or backend="aws". Takes a Models enum member (Models.H1, Models.M_OPTIMUS); the companion tissue model uses the string "tissue-seg". |
| Inference endpoint | A running model service that accepts tile requests and returns outputs — an on-premise container or a SageMaker endpoint. |
| SageMaker | AWS’s managed ML hosting service used for Bioptimus cloud deployment; the SDK reaches it via the /invocations dispatch endpoint. |
| Model package | The on-premise container variant — H1 (H-Optimus + tissue-seg) or M (M-Optimus + tissue-seg). |
Models & outputs
| Term | Meaning |
|---|---|
| Foundation model (FM) | A large model pre-trained on vast data that produces general-purpose representations reused across many downstream tasks. |
| H-Optimus | Bioptimus’s histology foundation model; outputs tile embeddings. Current version: H-Optimus-1. |
| M-Optimus | Bioptimus’s multimodal model; predicts spatial gene expression from histology (and optional bulk RNA), and also outputs embeddings. |
| Embedding | A numeric feature vector summarizing a tile. H-Optimus returns the CLS token of size 1536. |
| 1536-d | The dimensionality of H-Optimus and M-Optimus tile embeddings — a 1536-number feature vector per tile. |
| CLS token | The transformer’s summary output used as the tile embedding. H-Optimus returns the 1536-d CLS token; M-Optimus returns its MLP output. The embedding type is fixed (not user-selectable). |
| Spatial gene expression | Gene expression mapped to locations across a slide. M-Optimus predicts this from H&E tiles. |
Tissue segmentation (tissue-seg) | A companion model (bundled in both packages) that produces a binary tissue/background mask. |
Imaging & data
| Term | Meaning |
|---|---|
| WSI (whole slide image) | A digitized microscope slide — often gigapixel-scale — split into tiles for processing. |
| H&E | Hematoxylin and eosin, the standard tissue stain in routine pathology. |
| Tile (patch) | A fixed-size crop of a WSI. Embeddings use 224×224; tissue segmentation uses 512×512. |
| MPP (microns per pixel) | Physical image resolution. Embeddings use 0.5 µm/px; tissue segmentation uses 8.0 µm/px. |
| Bulk RNA-seq | Aggregate (non-spatial) gene expression for a sample. Optional input to M-Optimus prediction. |
| Ensembl gene ID | Standardized gene identifier (e.g. ENSG00000000003) used in bulk RNA inputs and gene-set metadata. |
| Zarr / HDF5 / NPZ | Output file formats the SDK writes per-slide results to. |
Infrastructure
| Term | Meaning |
|---|---|
| CUDA Compute Capability | An NVIDIA GPU architecture version. Compute Capability 8.6 on x86-64 runs the container out of the box; other architectures require recompilation. |
| NVIDIA Container Toolkit | Enables GPU access inside Docker containers (--gpus all). |
ml.g5.xlarge | The recommended SageMaker instance (a single 24 GB A10G-class GPU). |
Benchmarks
| Term | Meaning |
|---|---|
| PathBench | A multi-task, multi-organ pathology FM benchmark (HKUST). H-Optimus-1 ranks first overall. |
| HEST | A benchmark for predicting gene expression from histology (Harvard; Jaume et al. 2025), scored by Pearson correlation. |
| MIL (multiple instance learning) | A method that aggregates many tile-level features into a slide-level prediction. |