Weakly-supervised system, method and workflow for processing whole slide image for disease detection

Abstract

In generating training and testing datasets for machine learning, manual annotation of a large volume of data is impractical and expensive. A machine-learning model for detecting carcinoma (CA) from a whole slide image (WSI) processes average cellular features of cells identified on the WSI. Each cellular feature is a descriptive statistic of the cells, advantageously allowing the training and testing datasets to be constructed without a costly annotation process of pixelwise labelling each cell on a WSI training sample. Apart from predicting a CA case or a non-CA case for the WSI, the machine-learning model is also usable to: identify a suspicious CA case for priority assessment if a non-CA case is predicted for the WSI; generate a tumor probability heatmap of the WSI for visualizing potential CA regions on the WSI to assist pathological assessment; and assess quality control of a triage system before implementation in clinical setting.