Before revision:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images. Base models were DenseNet, Inception V3, and MobileNet and compared with PlexusNet. The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
After revision:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole slides images from the Camelyon 2016 challenge. PlexusNet-architecture -derived models were compared to models derived from several existing "state of the art" architectures. We measured discrimination accuracy, calibration, and clinical utility. An ablation study was conducted to study the effect of each component of PlexusNet on model performance. A well-fitted PlexusNet-based model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.