Crowdsourcing for pathology


One of the key hurdles preventing translation of advances in machine learning into the medical domain is lack of datasets for algorithm training and validation. Training accurate computational models is dependent on manual delineation (aka. annotation) of tissue regions and other microscopic elements in scanned slides of tumor biopsies. This task that is extremely time consuming and impractical given the time constraints of, and clinical demands on, practicing pathologists.


Develop algorithmic protocols and computational tools for systematic acquisition for manual delineations of region and cell nuclei from non-pathologists. Explore the accuracy and limitations of annotations from non-experts and develop custom modeling approaches to handle crowd data.


Mohamed Amgad
PhD Candidate
Lee Cooper, PhD
Principal Investigator


Amgad M, Elfandy H, Hussein H, Atteya LA, Elsebaie MA, Abo Elnasr LS, Sakr RA, Salem HS, Ismail AF, Saad AM, Ahmed J. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019 Sep 15;35(18):3461-7.


Advanced Development of an Open-Source Platform for Web-Based Integrative Digital Image Analysis in Cancer
NCI U24CA194362