The teacher model extracts local image features, whereas the student model focuses on global features using an attention mechanism.
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis.
In this paper, we propose a two-phase multi-expert classification method for human action recognition to cope with long-tailed distribution by means of super-class learning and without any extra information.
In this paper, we propose a domain adaptation approach to strengthen the contributions of the semantic background context.
This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, which aims to detect the boundaries.