Deep neural networks have achieved promising results in a breadth of medical image segmentation tasks. Nevertheless, they require large training datasets with pixel-wise segmentations, which are expensive to obtain practice. Mixed supervision could mitigate this difficulty, small fraction the data containing complete annotations, while rest being less supervised, e.g., only handful pixels label...

Deep neural networks (DNNs) have been used successfully for many image classification problems. One of the most important factors that determines final efficiency a DNN is correct construction training set. Erroneously labeled images can degrade accuracy and additionally lead to unpredictable model behavior, reducing reliability. In this paper, we propose MultiNET, novel method automatic detect...

Abstract Aim The benefits of peer-led teaching are well evidenced, as the interactive over passive learning in a clinical setting. This project aimed to create an FY1-led induction model that could be reproduced across departments, cost-effectively boosting junior trainee confidence, and improving continuity care during quarterly changeover periods with minimal pressure on senior clinicians. Me...

For successful communication, conversational partners need to estimate each other's current knowledge state. Nonverbal facial and bodily cues can reveal relevant information about how confident a speaker is about what they are saying. Using functional magnetic resonance imaging, we aimed to identify brain regions that encode how confident a speaker is perceived to be. Participants viewed videos...