A novel and bespoke two-stage computer vision model was developed and trained on high-resolution microscope slide images of FA and PT tissue samples.

– First stage of the pipeline creates smaller patches out of a large, gigapixel-scale whole-slide image, applies several preprocessing steps, and uses a convolutional neural network (CNN) to extract discriminative visual features at the patch level

– Second stage of the pipeline uses a recurrent neural network (RNN) to learn the spatial arrangement of the patch-level features within each slide, in order to aggregate them and produce an overall predicted class (FA or PT) for each whole-slide image