The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. ![]() This retrospective, multicenter study, evaluated 165 patients with brain metastases. The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data.
0 Comments
Leave a Reply. |