Full-Stack Web and Mobile Apps featuring a trendy and cozy café/restaurant.
A collaborative study with pathologists of LAC+USC/Keck Hospital to assess the diagnostic accuracy of convolutional neural networks trained on whole slide scanned cytology images.
Single-page Application using Typescript and Angular framework (Ver 4.x). Responsive UI design in Angular Material and Angular Flex-Layout. Demonstrates use of Observables, reactive programming with RxJS, and Restangular for communicating with a server supporting REST API.
Hybrid mobile application using Ionic framework (Ver 3.x) and Cordova hybrid application framework to target multiple mobile platforms with a single codebase. Features push notifications, sending emails and calls, and social media sharing.
Cross-platform, native iOS and Android app built with NativeScript (Ver 3.x). Features a truly native mobile UI feel, animations, gestures, and performance. Supports offline storage with SQLite and Couchbase
Full-fledged back-end RESTful API server developed using NodeJS, Express, MongoDB and Mongoose. Token-based user authentication with Passport-JWT. Includes HTTPS secure communication, form-based file upload with Multer, Cross-Origin Resource Sharing (CORS) and OAuth2 authorization for Social Authentication via Facebook credentials.
In partnership with pathologists of Los Angeles County + USC/Keck Hospital of USC, we aim to explore how the diagnostic performance of convolutional neural networks can be compared to the human-level expertise of licensed pathologists.
Mohammad Pourhomayoun, Ph.D. | Principal Investigator |
Sue Ellen Martin, MD, Ph.D. | Principal Investigator |
Melanie Kwon, MSCS | Research Project Manager |
Vanessa Martin, MD | Co-Investigator |
Tae Hun Kim, MD | Co-Investigator |
Adam Berman, MSCS | Co-Investigator |