Diagnosis of focal liver lesions from ultrasound using deep learning - 05/04/19
Abstract |
Purpose |
The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning.
Materials and methods |
We trained our algorithm on a dataset proposed during a data challenge organized at the 2018 Journées Francophones de Radiologie. The dataset was composed of 367 two-dimensional ultrasound images from 367 individual livers, captured at various institutions. The algorithm was guided using an attention mechanism with annotations made by a radiologist. The algorithm was then tested on a new data set from 177 patients.
Results |
The models reached mean ROC-AUC scores of 0.935 for FLL detection and 0.916 for FLL characterization over three shuffled three-fold cross-validations performed with the training data. On the new dataset of 177 patients, our models reached a weighted mean ROC-AUC scores of 0.891 for seven different tasks.
Conclusion |
This study that uses a supervised-attention mechanism focused on FLL detection and characterization from liver ultrasound images. This method could prove to be highly relevant for medical imaging once validated on a larger independent cohort.
Le texte complet de cet article est disponible en PDF.Keywords : Artificial intelligence, Deep learning, Focal liver lesions, Ultrasound, Radiology
Plan
Vol 100 - N° 4
P. 227-233 - avril 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.