This chapter provides an introductory summary of recent advancements in deep learning and its role in modern computer-aided diagnosis and treatment systems. The chapter elaborates on particular deep learning architectures, like convolutional neural networks and generative adversarial networks, and provides a minimum technical background before analyzing their applications in the field. In the second half, the chapter focuses on recent developments of medical deep learning applications in the specific field of maxillofacial imaging with a focus also on perceptual loss functions and uncertainty-aware deep learning.

Deep learning and generative adversarial networks in oral and maxillofacial surgery / Pepe, Antonio; Trotta, Gianpaolo Francesco; Gsaxner, Christina; Brunetti, Antonio; Cascarano, Giacomo Donato; Bevilacqua, Vitoantonio; Shen, Dinggang; Egger, Jan. - STAMPA. - (2021), pp. 55-82. [10.1016/B978-0-12-823299-6.00003-1]

Deep learning and generative adversarial networks in oral and maxillofacial surgery

Trotta, Gianpaolo Francesco;Brunetti, Antonio;Cascarano, Giacomo Donato;Bevilacqua, Vitoantonio;
2021-01-01

Abstract

This chapter provides an introductory summary of recent advancements in deep learning and its role in modern computer-aided diagnosis and treatment systems. The chapter elaborates on particular deep learning architectures, like convolutional neural networks and generative adversarial networks, and provides a minimum technical background before analyzing their applications in the field. In the second half, the chapter focuses on recent developments of medical deep learning applications in the specific field of maxillofacial imaging with a focus also on perceptual loss functions and uncertainty-aware deep learning.
2021
Computer-Aided Oral and Maxillofacial Surgery : Developments, Applications, and Future Perspectives
978-0-12-823299-6
Elsevier - Academic Press
Deep learning and generative adversarial networks in oral and maxillofacial surgery / Pepe, Antonio; Trotta, Gianpaolo Francesco; Gsaxner, Christina; Brunetti, Antonio; Cascarano, Giacomo Donato; Bevilacqua, Vitoantonio; Shen, Dinggang; Egger, Jan. - STAMPA. - (2021), pp. 55-82. [10.1016/B978-0-12-823299-6.00003-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/226326
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