Figure 1.

Overview of HCC-SurvNet. All WSI were preprocessed by discarding non tissue-containing white background using thresholding, then partitioned into non-overlapping tiles of size 299 × 299 pixels and color normalized. A tumor tile classification model was developed using the Stanford-HCCDET dataset, which contained WSI with all tumor regions manually annotated. The tumor tile classification model was subsequently applied to each tissue-containing image tile in the TCGA-HCC (n = 360 WSI) and Stanford-HCC (n = 198 WSI) datasets for inference. The 100 tiles with the highest predicted probabilities of being tumor tiles were input into the downstream risk prediction model to yield tile-based risk scores, which were averaged to generate a WSI-level risk score for recurrence. WSI, whole-slide image.