With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguation is at a historic peak, the number of paper authors is increasing every year, and the situation o...
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information ...
IEEE journal of biomedical and health informatics
40030272
In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treatment planning. Current deep learning-based medical image segmentation methods mainly adopt encoder-d...
Neural networks : the official journal of the International Neural Network Society
40024050
In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models...
Neural networks : the official journal of the International Neural Network Society
40010295
In the Domain Generalizable Person Re-Identification (DG Re-ID) task, the quality of identity-relevant descriptor is crucial for domain generalization performance. However, for hard-matching samples, it is difficult to separate high-quality identity-...
Neural networks : the official journal of the International Neural Network Society
40010289
The task of named entity typing (NET) on social platforms is significant as it involves identifying the various types of named entities within unstructured text. The existing methods for NET only utilize the text modality to classify the types of nam...
Neural networks : the official journal of the International Neural Network Society
39987714
Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the cl...
In previous tasks of glottis image segmentation, the position attention mechanism was rarely incorporated, neglecting the detailed information in glottis position detection. Aiming to improve the U-Net architecture, this study introduces the dual att...
IEEE journal of biomedical and health informatics
40030350
Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences in local textures, shapes, colors, etc. Learning consistent representations for anatomical semantics...
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data. However, existing TTA solutions for segmentation tasks suffer fr...