no code implementations • 11 Nov 2024 • Qiao Qiao, Yuepei Li, Qing Wang, Kang Zhou, Qi Li
Furthermore, to bridge the gap between KGs and PLMs, we employ a self-supervised representation learning method called BYOL to fine-tune PLMs with two different views of a triple.
no code implementations • 17 Sep 2024 • Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li
In this study, we investigate the impact of memory strength and evidence presentation on LLMs' receptiveness to external evidence.
1 code implementation • 10 Jul 2024 • Yuan Zhong, Chenhui Tang, Yumeng Yang, Ruoxi Qi, Kang Zhou, Yuqi Gong, Pheng Ann Heng, Janet H. Hsiao, Qi Dou
In this paper, we propose to collect dense weak supervision for medical image segmentation with a gaze annotation scheme.
1 code implementation • 22 Feb 2024 • Yuepei Li, Kang Zhou, Qiao Qiao, Qing Wang, Qi Li
We found that many of them rely on large validation sets and some used test set for tuning inappropriately.
no code implementations • 17 Feb 2024 • Hongye Zeng, Ke Zou, Zhihao Chen, Yuchong Gao, Hongbo Chen, Haibin Zhang, Kang Zhou, Meng Wang, Rick Siow Mong Goh, Yong liu, Chang Jiang, Rui Zheng, Huazhu Fu
Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.
1 code implementation • 1 Dec 2023 • Qing Wang, Kang Zhou, Qiao Qiao, Yuepei Li, Qi Li
We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs.
1 code implementation • 23 Aug 2023 • Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
no code implementations • 15 Jun 2023 • Qiao Qiao, Yuepei Li, Kang Zhou, Qi Li
Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample.
1 code implementation • 20 Apr 2023 • Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, Yan Zhang
We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.
no code implementations • 28 Dec 2022 • Hongye Zeng, Kang Zhou, Songhan Ge, Yuchong Gao, Jianhao Zhao, Shenghua Gao, Rui Zheng
We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner.
no code implementations • 31 Jul 2022 • Kang Zhou, Qiao Qiao, Yuepei Li, Qi Li
To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance.
1 code implementation • ACL 2022 • Kang Zhou, Yuepei Li, Qi Li
In this paper, we study the named entity recognition (NER) problem under distant supervision.
no code implementations • 5 Oct 2021 • Kang Zhou, Jing Li, Weixin Luo, Zhengxin Li, Jianlong Yang, Huazhu Fu, Jun Cheng, Jiang Liu, Shenghua Gao
To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.
no code implementations • 9 May 2021 • Hong-Ye Zeng, Song-Han Ge, Yu-Chong Gao, De-Sen Zhou, Kang Zhou, Xu-Ming He, Edmond Lou, Rui Zheng
Methods: The network was trained to detect vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images and validated on 100 images.
1 code implementation • ECCV 2020 • Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao
In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.
no code implementations • 11 Dec 2019 • Huihong Zhang, Jianlong Yang, Kang Zhou, Zhenjie Chai, Jun Cheng, Shenghua Gao, Jiang Liu
Firstly, our method trains a biomarker prediction network to learn the features of the biomarker.
no code implementations • 28 Nov 2019 • Kang Zhou, Shenghua Gao, Jun Cheng, Zaiwang Gu, Huazhu Fu, Zhi Tu, Jianlong Yang, Yitian Zhao, Jiang Liu
With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images.
3 code implementations • 7 Mar 2019 • Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Ranked #1 on
Optic Disc Segmentation
on Messidor
no code implementations • 31 Aug 2018 • Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu
To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.