Deep learning for digital pathology: A critical overview of methodological framework
Meghdad Sabouri Rad, Junze Huang, Mohammad Mehdi Hosseini, Rakesh Choudhary, Harmen Siezen, Ratilal Akabari, Tamara Jamaspishvili, Ola El‐Zammar, Palak Patel, Saverio J. Carello, Michel R. Nasr, Bardia Rodd
Abstract
Deep learning frameworks have transformed the field of digital pathology by automating complex tasks and revealing intricate patterns within histopathological data. These advanced methodologies provide exceptional accuracy and scalability, facilitating the analysis of high-dimensional whole-slide images with unparalleled precision. In this article, we present a comprehensive deep learning framework highlighting recent advancements in computational pathology. We critically examine mathematical innovations and offer a comparative analysis of various models demonstrating the significant and ongoing improvements in the field.