Using Deep Learning Algorithms to Automatically Identify the Brain MRI Contrast: Implications for Managing Large Databases

By Ricardo Pizarro, Haz-Edine Assemlal, Dante De Nigris, Colm Elliott, Samson B. Antel, Douglas L. Arnold, Amir Shmuel. 2018

28 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neurology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

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

In Cognitive Neuroscience

1. Arvid Lundervold

In Neurology

1. Arvid Lundervold
2. Dan Boneh
3. Florian Tramèr

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “st set of 2100 labeled images. Medical CBIR is now, however, dominated by deep learning algorithms [311, 312, 313]. As an example, by retrieving medical cases similar to a given image, Pizarro et al. [279] developed a CNN for automatically inferring the contrast of MRI scans based on the image inten”