Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.

ABSTRACT: Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by res

SUBMITTER: Rong R 

PROVIDER: S-EPMC10123520 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature