Skip to main content Skip to secondary navigation

Software Tools

Main content start
Tool Description

MD.ai

An  app to jumpstart your deep learning project with features such as: 

  • Cloud storage of anonymized medical images

  • Web-based annotation tools optimized for medical deep learning

  • Real-time collaboration with your team

  • Easy export of annotations, images, and labels for training

  • URL links for assigning cases to team members

ePAD Imaging Platform

A freely available quantitative imaging informatics platform, developed by the Rubin Lab at Stanford Medicine Radiology at Stanford University. Thanks to its plug-in architecture, ePAD can be used to support a wide range of imaging-based projects.

STARR Cohort Discovery

The STARR Cohort Discovery Tool is developed by Research IT and provisioned by Research Informatics Center for the researcher. This tool is used to find out how many Stanford patients match a particular clinical phenotype, and to learn more about this set of patients. Only aggregated, approximate results are provided so this can be used in the preparatory to research phase.

STARR Chart Review

The STARR Chart Review tool is developed by Research IT and provisioned by Research Informatics Center for the researcher. This tool provides an IRB compliant view of Stanford patient data. Researchers can review all labs, pharmacy orders, billing codes, and clinical transcripts for the list of patients relevant to their research. The tool also comes with a continuously updated cohort demographics dashboard, the ability to hide/show defined categories of clinical data, and the ability to search an entire patient record for specific keyword text or phrases.

Montage

The Montage tool provides the ability to search report text from the Radiology clinical system.  This can be helpful if used in conjunction with STARR Cohort Discovery to find out how many Stanford radiology studies match particular search variables. 

Google Cloud Platform Data Labeling

AI Platform Data Labeling Service through GCP, lets you work with human labelers to generate highly accurate labels for a collection of data that you can use in machine learning models.

Find instructions for creating instructions for human labelers here

Federated Learning

Tensorflow Federated Learning.

Federated Learning on mobile devices