Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments.
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering.
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).
In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.
Ranked #2 on Named Entity Recognition on WNUT 2016
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.
Ranked #2 on Chinese Named Entity Recognition on Resume NER
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.