In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.
Purpose The accuracy of the segmentation of the target lesion and at-risk surrounding organs is important for cervical cancer patients treated with three-dimensional (3D) brachytherapy. However, the nature of brachytherapy, organ deformities, metal induced artifacts caused by applicators, and limited operating time make this process a challenge. Deep learning segmentation has recently emerged as an approach to this problem. The basic concept proposed in this paper is accurate segmentation of a pelvic computed tomography image dataset such that patients can obtain more accurate radiotherapy. Methods In this work, we propose a solution based on 3D U-Net and Long Short-Term Memory (LSTM). The model was trained and tested on a computed tomography pelvic image dataset comprising 51 patients who underwent 3D brachytherapy. The organs that required segmentation included the bladder, bowels, sigmoid colon, rectum, and uterus. We also used self-ensemble to improve segmentation accuracy. The Dice coefficient was used as the evaluation metric to determine the segmentation results for all of the organs under consideration. ResultsThe proposed model was evaluated using organ segmentation obtained from 10 patients with newly diagnosed stage I-IVA cervical cancer. The test results showed that segmentation conducted using the proposed model resulted in the following Dice coefficient values (mean, ± standard deviation): 87 (± 6.3) %, 72 (± 9.1) %, 72 (± 7.8) %, 95 (± 3.5) %, 86 (± 4.9) %, 77 (± 8.4) %, 73 (± 10.2) %, and 93 (± 3.5) % for the HRCTV, GTV, bowels, foley, bladder, rectum, sigmoid colon, and uterus, respectively. Conclusion This method shows that the combination of 3D U-Net and LSTM and self-ensemble post-processing has high potential for segmentation of a pelvic computed tomography dataset.
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