However, the performance of the dynamic filter might be degraded since simple feature pooling is used to reduce the computational resource in the IDF part.
no code implementations • 20 Apr 2022 • Kelly Payette, Hongwei Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara, Bella Specktor Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Li Xuesong, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, Kuanlun Liao, Yixuan Wu, Jintai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e. g., diseases and chemicals) from the ever-growing biomedical literature.
To address this issue, we propose a novel GAN model, i. e., AU-GAN, which has an asymmetric architecture for adverse domain translation.
This lightweight dynamic filter is applied to the front-end of KWS to enhance the separability of the input data.
We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).
The recent success of question answering systems is largely attributed to pre-trained language models.
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #2 on Named Entity Recognition on NCBI-disease
With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.