Diagnosis of focal liver lesions from ultrasound using deep learning.
Diagn Interv Imaging. 2019 Apr;100(4):227-233. doi: 10.1016/j.diii.2019.02.009. Epub 2019 Mar 27.
Schmauch B1, Herent P1, Jehanno P2, Dehaene O3, Saillard C1, Aubé C4, Luciani A5, Lassau N6, Jégou S1.
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.
Read Full Article Here: https://doi.org/10.1016/j.diii.2019.02.009