The Chinese University of Hong Kong

The Hong Kong Polytechnic University

Ka Fai To

Ngai Nick Alex Wong

Jung Sun Yoo

Cheong Kin Ronald Chan

Ho Yin Martin Yeung

Kwok Chiu Cheung

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.

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Hong Kong, China

Ka Fai To