No abstract

Privacy protection, high labeling cost, and the varying characteristics of seizures among patients and at different times are the main obstacles to building seizure detection models. In light of these, we propose a novel Mentor-Student architecture for Patient-Specific seizure detection (MS4PS). It contains a new way of knowledge transferring named mentor-select-for-student, which exploits the knowledge of a Mentor model by using this model to select data for training a Student model, making it possible to avoid transferring patients' data and the negative influence of transferring parameters/structures of pretrained models. It also contains a new way of active learning, which uses both an experienced Mentor model and a quick-learning Student model to select high-quality samples for doctors to label. Each of the two models is coupled with a particular sample selection strategy that combines the uncertainty/certainty and the distance between unlabeled samples and labeled seizure samples. The proposed method could quickly train a suitable detector for a patient at his/her first epilepsy diagnosis with the help of: (1) an experienced Mentor model that chooses the most category-certain electroencephalography (EEG) data segments; (2) a Student model (detector itself) that chooses the most category-uncertain EEG data segments; (3) doctors who label these data segments selected by both the Mentor model and Student model. By replacing or improving the Mentor model and refining the historical models of patients when they come next time, the MS4PS system could be sustainedly promoted. The proposed method is tested on CHB-MIT and NEO datasets and the results demonstrate its effectiveness and efficiency.

Visible-Infrared person re-identification (VI-ReID)