A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models

Author:

Morilla IanORCID

Funder

Agence Nationale de la Recherche

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference37 articles.

1. Agarap A (2018) Deep learning using rectified linear units (ReLU). CoRR abs/1803.08375. arXiv:1803.08375

2. Apley D (2001) Visualizing the effects of predictor variables in black box supervised learning models. Ann Stat 8(2):1189–1232

3. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: JMLR: workshop and conference proceedings, vol 27, pp 37–50

4. Barkmeier D, Dillman J, Al-Hawary M, Heider A, Davenport M, Smith E, Adler J (2016) Mr enterography–histology comparison in resected pediatric small bowel crohn disease strictures: can imaging predict fibrosis? Pediatr Radiol 4:498–507

5. Ben Amar M, Bianca C (2016) Towards a unified approach in the modeling of fibrosis: a review with research perspectives. Phys Life Rev 3:917–932

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