1 code implementation • 27 Mar 2025 • Xiaoqin Wang, Xusen Ma, Xianxu Hou, Meidan Ding, Yudong Li, Junliang Chen, WenTing Chen, Xiaoyang Peng, Linlin Shen
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks.
no code implementations • 14 Oct 2024 • Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu
The proposed models were evaluated on three downstream tasks and achieved better or comparable performance compared to deep learning models, generalized LLMs, and task fine-tuned LLMs.
1 code implementation • 17 Apr 2024 • Meng Jiang, Yi Jing Yu, Qing Zhao, Jianqiang Li, Changwei Song, Hongzhi Qi, Wei Zhai, Dan Luo, Xiaoqin Wang, Guanghui Fu, Bing Xiang Yang
Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care.
1 code implementation • 31 Aug 2022 • Xiaoqin Wang, Chen Chen, Rushi Lan, Licheng Liu, Zhenbing Liu, Huiyu Zhou, Xiaonan Luo
Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space.
no code implementations • 4 May 2022 • Youhuan Yang, Lei Sun, Leyu Dai, Song Guo, Xiuqing Mao, Xiaoqin Wang, Bayi Xu
Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i. e., not approaching the lower bound of robustness).
no code implementations • 4 May 2022 • Youhuan Yang, Lei Sun, Leyu Dai, Song Guo, Xiuqing Mao, Xiaoqin Wang, Bayi Xu
This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i. e., the accuracy under adversarial attacks).
no code implementations • 6 Oct 2020 • Gongbo Liang, Connor Greenwell, Yu Zhang, Xiaoqin Wang, Ramakanth Kavuluru, Nathan Jacobs
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples.
no code implementations • 9 Sep 2020 • Gongbo Liang, Yu Zhang, Xiaoqin Wang, Nathan Jacobs
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain.
no code implementations • 2 Mar 2020 • Yu Zhang, Gongbo Liang, Nathan Jacobs, Xiaoqin Wang
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images.
no code implementations • 27 Feb 2020 • Yu Zhang, Xiaoqin Wang, Hunter Blanton, Gongbo Liang, Xin Xing, Nathan Jacobs
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice.
no code implementations • 27 Feb 2020 • Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females.