Deep Learning Research Directions in Medical Imaging

Cristian Simionescu () and Adrian Iftene
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Cristian Simionescu: Faculty of Computer Science, Alexandru Ioan Cuza University, 700483 Iasi, Romania
Adrian Iftene: Faculty of Computer Science, Alexandru Ioan Cuza University, 700483 Iasi, Romania

Mathematics, 2022, vol. 10, issue 23, 1-25

Abstract: In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for such tasks require a large amount of labeled data. It is often difficult to procure such data due to the fact that these requires experts to manually label them, in addition to the privacy and legal concerns that limiting their collection. Due to this, creating self-supervision learning methods and domain-adaptation techniques dedicated to this domain is essential. This paper reviews concepts from the field of deep learning and how they have been applied to medical image analysis. We also review the current state of self-supervised learning methods and their applications to medical images. In doing so, we will also present the resource ecosystem of researchers in this field, such as datasets, evaluation methodologies, and benchmarks.

Keywords: deep learning; medical image analysis; self-supervised learning; diagnosis; brain cancer; tuberculosis; Alzheimer’s disease (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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