WSInfer: blazingly fast inference on whole slide images

🔥 🚀 WSInfer is a blazingly fast pipeline to run patch-based classification models on whole slide images. It includes several built-in models, and it can be used with any PyTorch model as well. The built-in models are listed below.

Running inference on whole slide images is done with a single command line:

wsinfer run \
   --wsi-dir slides/ \
   --results-dir results/ \
   --model breast-tumor-resnet34.tcga-brca

See all of the available trained models with

wsinfer-zoo ls

To get started, please install WSInfer and check out the User Guide. To get help, report issues or request features, please submit a new issue on our GitHub repository. If you would like to make your patch classification model available in WSInfer, please get in touch with us! You can submit a new GitHub issue.

Citation

If you find our work useful, please cite our paper https://doi.org/10.1038/s41698-024-00499-9.

Kaczmarzyk, J.R., O’Callaghan, A., Inglis, F. et al. Open and reusable deep learning for pathology with WSInfer and QuPath. npj Precis. Onc. 8, 9 (2024). https://doi.org/10.1038/s41698-024-00499-9

Original H&E	Heatmap of Tumor Probability

Contents:

• Installing
  • Prerequisites
  • Install WSInfer
  • Supported slide backends
  • Containers
  • Developers
  • Getting started
• User Guide
  • Video walkthrough
  • Getting help
  • Get help on the command line
  • List available models
  • Run model inference
  • Outputs of model inference
  • Run model inference in containers
  • Use your own model
  • Convert model outputs to Stony Brook format (QuIP)
• CLI
  • run
  • tosbu
  • patch
• QuPath Extension
• API Reference
  • wsinfer

Available models

After installing wsinfer, use the following command to list the most up-to-date models:

wsinfer-zoo ls

The table below may be incomplete.

Classification task	Output classes	Architecture	Dataset	Resolution (px @ um/px)	Reference
Breast adenocarcinoma detection	no-tumor, tumor	ResNet34	TCGA BRCA	350 @ 0.25	Ref
Colorectal tissue classification	background, normal_colon_mucosa, debris, colorectal_adenocarcinoma_epithelium, adipose, mucus, smooth_muscle, cancer_associated_stroma, lymphocytes	ResNet50 (trained by TIAToolbox dev team)	NCT-CRC-HE-100K	224 @ 0.5	Ref
Lung adenocarcinoma detection	lepidic, benign, acinar, micropapillary, mucinous, solid	ResNet34	TCGA LUAD	350 @ 0.5	Ref
Lymph node metastasis detection in breast cancer	nomets, mets	ResNet50 (trained via TIAToolbox dev team)	PatchCamelyon	96 @ 1.0	Ref
Lymphocyte detection	til-negative, til-positive	InceptionV4 (without batchnorm)	23 TCGA studies	100 @ 0.5	Ref
Pancreatic adenocarcinoma detection	tumor-positive	Preactivation ResNet34	TCGA PAAD	350 @ 1.5	Ref
Prostate adenocarcinoma detection	grade3, grade4or5, benign	ResNet34	TCGA PRAD	175 @ 0.5	Ref

Indices and tables

• Index

• Module Index

• Search Page