Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence in Surgery: Neural Networks and Deep Learning
0
Zitationen
4
Autoren
2020
Jahr
Abstract
Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such computational methods. However, surgeons and computer scientists should partner to develop and assess deep learning applications of value to patients and healthcare systems. This chapter and the accompanying hands-on material were designed for surgeons willing to understand the intuitions behind neural networks, become familiar with deep learning concepts and tasks, grasp what implementing a deep learning model in surgery means, and finally appreciate the specific challenges and limitations of deep neural networks in surgery. For the associated hands-on material, please see https://github.com/CAMMA-public/ai4surgery.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.500 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 7.611 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.088 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.881 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.598 Zit.