Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices-A Feasibility Study.

ABSTRACT: Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then e

SUBMITTER: Sager P 

PROVIDER: S-EPMC8465488 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature