1 code implementation • 2 May 2021 • Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qianni Zhang, Qun Jin, Lingling Sun, Qisi Lian, Neng Xia, Ruizi Peng, Kai Tang, Yaqi Wang, Shuai Wang
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.
no code implementations • 7 Mar 2021 • Yunxiang Li, Yifan Zhang, Yaqi Wang, Shuai Wang, Ruizi Peng, Kai Tang, Qianni Zhang, Jun Wang, Qun Jin, Lingling Sun
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.
1 code implementation • 11 Nov 2020 • Jingxiong Li, Yaqi Wang, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun
Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.
no code implementations • 1 May 2020 • Dailin Lv, Wuteng Qi, Yunxiang Li, Lingling Sun, Yaqi Wang
Then we used SEME-DenseNet169 for fine-grained classification of viral pneumonia and determined if it is caused by COVID-19.
no code implementations • 1 May 2020 • Yaqi Wang, Lingling Sun, Yifang Zhang, Dailin Lv, Zhixing Li, Wuteng Qi
In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays.
no code implementations • 13 Aug 2018 • Guilherme Aresta, Teresa Araújo, Scotty Kwok, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Bahram Marami, Marcel Prastawa, Monica Chan, Michael Donovan, Gerardo Fernandez, Jack Zeineh, Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust, Quoc Dang Vu, Minh Nguyen Nhat To, Eal Kim, Jin Tae Kwak, Sameh Galal, Veronica Sanchez-Freire, Nadia Brancati, Maria Frucci, Daniel Riccio, Yaqi Wang, Lingling Sun, Kaiqiang Ma, Jiannan Fang, Ismael Kone, Lahsen Boulmane, Aurélio Campilho, Catarina Eloy, António Polónia, Paulo Aguiar
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.