5 accepted papers
Existing deep learning-based Multi-focus Image Fusion (MFIF) methods often rely on loss functions derived from linear combinations of image quality metrics, leading to complexities in training and only marginal improvements in image quality. Recognizing this, our study identifies input space and sca…
Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) str…
In the context of medical image segmentation, complementary information among multi-modality images can improve segmentation performance. However, acquiring the complete multi-modality data in clinical settings is difficult. To tackle this problem, we propose a novel multi-modality knowledge distill…
Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled…
Ontology serves as the foundation for the underlying representation of knowledge. In order to achieve the sharing of knowledge across languages, ontologies that are typically only represented in English must be translated into different languages. Building a domain-specific translation system is nec…