Editorial

Abstract

In the beginning, we had only a few authors and manuscripts, sometimes no manuscripts at all in the works, and I was beating the bushes for papers and for reviewers. We worked with paper (remember that media?) so all manuscripts were mailed (not emailed) to me, then copied and sent to reviewers. Reviews were submitted

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

“…Over the last two decades, numerous research papers on CAD systems were published. Automated systems for digital histopathological imaging can maintain reproducibility and consistency using suitable image processing techniques [ 41 ]. In fact, there are many research perspectives for CAD systems applied in the histopathological domain, including: (I) cancer detection in the given tissue, (II) automatic grading to correctly quantify the level of the malignancy, which can offer more insights into disease characterization, (III) cell/nuclei/gland segmentation that discovers and separates these regions from images, and (IV) multi-class classification for the different subtypes of a specific type of cancer.…”

“…Automated prostate cancer diagnosis using histopathology images is deemed to offer great promise for advanced cancer therapy, however, it is not a simple task, as several open scientific challenges have to be overcome before the CAD system of histopathology images can become part of the routine healthcare diagnostic pipeline. These challenges occur because of the numerous technical and computational variabilities and artifacts incurred due to differences in slide preparation and because of the complicated structure of the tumor tissues architecture [ 41 ]. Image analysis techniques are substantially reliant on the quality of the digital slide images.…”

“…At each stage, unwanted anomalies that are unassociated with the underlying biological factors, could be represented by differences in specimen preparation, staining, and even scanning with equipment from different vendors. For instance, when specimen sections are placed onto the slides, they may be folded and rumpled; dust may besmear the slides during scanning process; loss of microscope focus leads to blurred regions, noise, and shadows; and occasionally tissue regions are marked by color markers or chromatic aberrations [ 8 , 41 ]. Learning without considering these artifacts, as shown in Figure 4 , may deteriorate the performance of decision support algorithms.…”

“…Tissue autofluorescence differences in microscopic setup, staining protocol, and organ size could generate irregular lighting across the tissue samples. Additionally, the scanner’s sensitivity varies for different wavelengths of the light spectrum [ 41 ]. Large variations in light are considered an important factor for the precise prostate cancer diagnosis.…”

“…Many surveys have been published in recent years reviewing histopathological image analysis covering its history, and detailed information of general artificial intelligence techniques [ 7 , 8 , 12 , 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]; the main limitation is the lack of comprehensive surveys of histopathological image analysis that focus on prostate cancer [ 1 , 43 , 44 ]. Accordingly, in this survey we present more prostate histopathology from an image analysis point of view.…”