Advancing Glioblastoma Diagnosis through Innovative Deep Learning Image Analysis in Histopathology

Abstract

It has been the practice of human pathologists to diagnose pathology by examining stained specimens in a slide using a microscope for decades. Recently, several technologies have been developed to digitize the full pathological slide in order to streamline the labor-intensive process of manual labeling and categorization of pathological slides. Machine learning algorithms have been used to examine these digital slices for applications such as diagnosis in many cases. Many digital pathological image analysis algorithms rely on generic image recognition technologies, such as face recognition. A number of specific processing techniques are often required because digital pathological images and tasks differ greatly from facial features. Because of this, a new machine-learning technique has been developed specifically for histopathological images in order to differentiate glioblastoma slices from non-glioblastoma slices, thereby improving pathology diagnosis efficiency and labor intensity.