no code implementations • 11 Nov 2023 • Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi, Hatice Ceylan Koydemir, Alin Achim
In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis.
no code implementations • 25 Nov 2022 • Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi, Alin Achim
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment.
1 code implementation • 25 Apr 2021 • Chaozhuo Li, Bochen Pang, Yuming Liu, Hao Sun, Zheng Liu, Xing Xie, Tianqi Yang, Yanling Cui, Liangjie Zhang, Qi Zhang
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
no code implementations • 21 Mar 2021 • Tianqi Yang, Oktay Karakuş, Nantheera Anantrasirichai, Alin Achim
In the field of biomedical imaging, ultrasonography has become increasingly widespread, and an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable.
2 code implementations • 15 Jan 2021 • Jason Yue Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, Huasha Zhao
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks.
1 code implementation • 23 Nov 2020 • Carlos Mauricio Villegas Burgos, Tianqi Yang, Nick Vamivakas, Yuhao Zhu
Deep learning using Convolutional Neural Networks (CNNs) has been shown to significantly out-performed many conventional vision algorithms.
no code implementations • 12 Feb 2020 • Tianqi Yang, Oktay Karakuş, Alin Achim
A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution.