Weakly-Supervised System, Method and Workflow for Processing Whole Slide Image for Disease Detection

A computer-implemented method for processing a whole slide image (WSI) to detect carcinoma (CA), the method comprising:

• setting up a machine-learning model for classifying the WSI as a CA case or as a non-CA case, the machine-learning model being realized as a plurality of ensembled networks with a classification decision made by the machine-learning model according to a plurality of probabilities of having malignancy respectively generated by the plurality of ensembled networks, an individual ensembled network being realized as a multilayer perception (MLP) network configured by a plurality of hyperparameters, the plurality of hyperparameters being learnable, wherein the machine-learning model is arranged to process a plurality of averaged cellular features for CA detection, and wherein an individual averaged cellular feature is a descriptive statistic of cells identified on the WSI, allowing a training dataset for training the machine-learning model and a testing dataset for verifying the trained machine-learning model to be constructed without a need to involve a costly annotation process of pixelwise labelling each cell on a WSI training sample;
• and
• using the training and testing datasets to learn respective pluralities of hyperparameters for the plurality of ensembled networks and to train the plurality of ensembled networks.