Publications by authors named "Kobiljon Ikromjanov"
Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise.
View Article and Find Full Text PDFArticle Synopsis
- The study focuses on identifying biomarkers to differentiate prostate cancer (PCa) grade groups by analyzing cell nucleus clusters in histopathological sections using computer-based methods.
- Researchers utilized both traditional (unsupervised) and modern (supervised) AI techniques for cell nuclei segmentation, clustering, and classification, employing algorithms like minimum spanning tree and K-medoids.
- The findings highlight the potential of cluster features in cancer grading, but indicate that further validation is needed to improve classification accuracy between different grades of PCa.