Accurate small bowel lesions detection in wireless capsule endoscopy images using deep recurrent attention neural network

Rémi Vallée (1, 2) , Antoine Coutrot (1, 2) , Nicolas Normand (1, 2) , Harold Mouchère (1, 2)

1 LS2N - Laboratoire des Sciences du Numérique de Nantes
2 LS2N - équipe IPI - Image Perception Interaction

Wireless capsule endoscopy (WCE) allows medical doctors to examine the interior of the small intestine with a non-invasive procedure. This methodology is particularly important for Crohn's disease (CD), where an early diagnosis improves treatment outcomes. However, the viewing and evaluation of WCE videos is a time-consuming process for the medical experts. In this work, we present a recurrent attention neural network for the detection in WCE images of CD lesions in the small bowel. Our classifier reaches 90.85% accuracy on our own dataset annotated by experts from the Hospital of Nantes. The model has also been tested on a public endoscopic dataset, the CAD-CAP database used for the GIANA competition, and achieves high performance on detection task with an accuracy of 99,67%. This automatic lesion classifier will greatly reduce the amount of time spent by gastroenterologists in reviewing WCE videos, which will likely foster the development of this technique and speed-up the diagnosis of CD.

Mots clés: medical images classification; Deep learning; recurrent attention model; Crohn's disease

Domaines: Traitement des images [eess.IV]; Imagerie médicale; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Intelligence artificielle [cs.AI]; Imagerie

Rémi Vallée, Antoine Coutrot, Nicolas Normand, Harold Mouchère. Accurate small bowel lesions detection in wireless capsule endoscopy images using deep recurrent attention neural network. IEEE 21st International Workshop on Multimedia Signal Processing (MMSP 2019), Sep 2019, Kuala Lumpur, Malaysia. ⟨hal-02296282⟩