A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images - Mahesh Anil Inamdar; U. Raghavendra; Anjan Gudigar; Sarvesh Bhandary; Massimo Salvi; Ravinesh C. Deo; Prabal Datta Barua; Edward J. Ciaccio; Filippo Molinari; U. Rajendra Acharya (IEEE Access, 2023)
One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize medicine by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven healthcare a very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect important areas. We compare our results with a host of contemporary techniques and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model we propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications.