Deep learning for automated skeletal bone age assessment in X‐ray images

By Concetto Spampinato, Simone Palazzo, Daniela Giordano, Marco Aldinucci, Rosalia Leonardi. Medical Image Anal., 2017

394 citations recorded by the source corpus.

Subfields: Archeology, Oral Surgery, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2de3826ac48c1d3fca9ef38276c5f559e4742d31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Archeology

1. Vladimir I. Iglovikov
2. Alexey A. Shvets
3. Alexandr A Kalinin
4. Alexander Rakhlin

In Radiology, Nuclear Medicine and Imaging

1. Vladimir I. Iglovikov
2. Alexey A. Shvets
3. Alexandr A Kalinin
4. Alexander Rakhlin
5. V. Seethalakshmi
6. S. Suganyadevi
7. K. Balasamy

Citations in the corpus

• Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017, background): “Deep neural network based solutions for bone age assessment from hand radiographs were suggested before [13, 14,20].”
• A review on deep learning in medical image analysis (2021, background): “In contrast to any individual model, the goal of ensemble learning is to accomplish better execution with the group of models [97].”