Deep Learning-Integrated Digital Pathology System for Early-Stage Cancer Screening Using High-Resolution Tissue Images

Histopathological screening of early cancer is limited by manual readings, excessive workloads, and diagnostic inconsistency in the analysis of high-resolution images of tissues. To overcome these drawbacks, a digital pathology architecture with deep learning is proposed to conduct automated screening of malignancies both at the patch and slide scales. The structures use adaptive gigapixel patch partition, multi-scale convolutional feature encoding, attention-directed discriminative region amplification, and probability-based patch-To-slide aggregation. Sensitivity to early malignant patterns is improved by using a hybrid optimization loss. Large-scale digital pathology data analysis reveals that patch-level sensitivity is 94.82% and slide-level sensitivity is 97.35% with corresponding specificities of 93.47% and 95.91%. The system has a balanced accuracy of 96.63%, lower false screening rates of 2.65, and an average latency of 56.9 seconds per whole-slide image. These findings show that the presented system allows relevant, interpretable, and computationally effective screening of early-stage cancer that can be conducted in a clinical pathology setting.