SlideTap Project
Overview
SlideTap is a comprehensive platform designed for managing and processing digital pathology images and metadata into research datasets. It provides tools for image preprocessing, metadata management, and project-based workflows. The platform is built using FastAPI for the web application, Celery for task scheduling and processing, and Node for the web client.
Features
- Metadata Management: Allows importing, exporting, and curating of metadata.
- Image Processing: Supports various image processing steps including DICOM conversion and metadata extraction.
- Project Management: Facilitates the creation and management of projects, batches, and datasets.
Components
Web Client
The web client provides a graphical interface enabling the user to:
- Login to the application
- Create projects and batches
- Search and curate metadata
- Process images
- Finalize projects into datasets
Web Application
The web application handles the requests from the web client:
- User authentication and authorization
- Project and batch management
- Image and metadata processing
Task Processing
Long running tasks can be handled in the background using tasks:
- Metadata import and export
- Image preprocessing and postprocessing
See tasks for more information.
Dataset and Site-Specific Implementation
The platform can, and must, be adopted to the type of dataset to create and the site to extract metadata and images from:
- A schema is used to define the dataset structure
- Metadata and image importers and processors are used to injest site data into the platform
- Metadata and image exporters and processors are used to format curated data into a complete dataset
See site implementations and dataset implementations for further details.
Example application
For an ready-made example application, see example.
License
SlideTap: Copyright 2024 Sectra AB, licensed under Apache 2.0.
Acknowledgement
This project is part of a project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 945358. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. IMI website: