Medical Imaging and Radiology in Explainable Deep Learning

Medical Imaging and Radiology in Explainable Deep Learning

Copyright: © 2024 |Pages: 21
DOI: 10.4018/979-8-3693-4143-8.ch011
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Abstract

This chapter delves into the intricacies of medical imaging and ultrasound, where interpretable deep learning methodologies emerge as invaluable tools. It elucidates the utilization of deep learning models to extract radiomic features, discern their clinical significance, and various methodologies for incorporating them into structures that are comprehensible. The study underscores the criticality of comprehending radiomic features and their pivotal role in facilitating accurate diagnoses and informed treatment decisions. The primary objective of this chapter is to attain an intricate understanding of deep learning methodologies tailored explicitly for healthcare AI, with a focal point on radiologists and medical images.
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Interpretable Radiomics

In the vast expanse of advanced artificial intelligence (AI), the juncture where the realms of medical images and radiologists converge with explicable deep learning stands as a crucible of paramount significance. This nexus serves as a bridge, linking the tapestry of human knowledge with the precision of computer algorithms. It begets a milieu where healthcare practitioners can harness the prowess of deep learning models while retaining the ability to fathom their intricacies – a vital attribute for wielding sagacious decision-making faculties (Gal & Ghahramani, 2016).

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