Developing quality ground truth for machine-learning algorithms in pathology
Uploaded May 20, 2020.
A brief introductory video of some of the tools being developed to provide quality ground truth for machine-learning algorithms in pathology. This work is funded by grant U01CA220401.
Educational Paper Discussion: Management of Adult Rhabdomyosarcoma (Arabic)
Presented online via Zoom on Saturday, Feb 22, 2020, 1 PM EST.
The American Board Certified Doctors for Egypt (ABCDE) hosted an online session discussing the following paper: Elsebaie et al. 2018. The paper caters to medical graduates interested in oncology practice and clinical research, with an introductory explanation of how to interpret and critically appraise survival analysis papers.
Unbiased learning from large-scale histology images: the case for crowdsourcing
Co-presented at the Computer Science Department Seminar, Emory University, on Friday, February 8, 2019, 10:30 am EST.
We describe a study (Amgad et al, 2019) that coordinated pathologists, residents, and students to generate tens of thousands of image annotations to provide training and validation data for convolutional models in breast cancer. This dataset is the largest of its kind, and enabled the development of realistic state-of-the-art performance for semantic segmentation of breast cancer images.
Predicting cancer outcomes with machine learning
Co-presented at Department of Biomedical Informatics, Emory University Academic Exchange seminar, on Oct 17, 2017.
We describe two studies (Yousefi et al, 2017, and Mobadersany et al, 2018) that rely on deep-learning models to predict cancer outcomes (specifically, overall patient survival) using histology imaging data and/or genomics (gene expression, mutations, chromosomal aberrations, etc). The studies used brain cancer (glioma) and gynecological cancer (mainly breast cancer) data from The Cancer Genome Atlas.