• Massimo Salvi, U Rajendra Acharya, Filippo Molinari, and Kristen M Meiburger.
• Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca Degli Abruzzi 24, Turin, 10129, Italy. Electronic address: massimo.salvi@polito.it.
• Comput. Biol. Med. 2021 Jan 1; 128: 104129.

AbstractRecently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field.Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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