• Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders 

      Yang, Shuo; Yin, Zhong; Wang, Yagang; Zhang, Wei; Wang, Yongxiong; Zhang, Jianhua (Computers in Biology and Medicine;Volume 109, June 2019, Journal article; Peer reviewed, 2019-04-26)
      To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing ...
    • Meta-learning with implicit gradients in a few-shot setting for medical image segmentation 

      Khadka, Rabindra; Jha, Debesh; Riegler, Michael A.; Hicks, Steven; Thambawita, Vajira; Ali, Sharib; Halvorsen, Pål (Computers in Biology and Medicine;Volume 143, April 2022, 105227, Peer reviewed; Journal article, 2022-02-03)
      Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited ...