1 code implementation • 19 Jan 2024 • Zhengliang Liu, Jason Holmes, Wenxiong Liao, Chenbin Liu, Lian Zhang, Hongying Feng, Peilong Wang, Muhammad Ali Elahi, Hongmin Cai, Lichao Sun, Quanzheng Li, Xiang Li, Tianming Liu, Jiajian Shen, Wei Liu
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
no code implementations • 2 Dec 2023 • Lian Zhang, Jason M. Holmes, Zhengliang Liu, Hongying Feng, Terence T. Sio, Carlos E. Vargas, Sameer R. Keole, Kristin Stützer, Sheng Li, Tianming Liu, Jiajian Shen, William W. Wong, Sujay A. Vora, Wei Liu
The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89. 32%$\pm$1. 45% vs. 93. 48%$\pm$1. 51% vs. 96. 79%$\pm$0. 83%, OARs: 85. 87%$\pm$1. 73% vs. 91. 15%$\pm$1. 13% vs. 94. 29%$\pm$1. 01%).
no code implementations • 5 Oct 2023 • Jason Holmes, Lian Zhang, Yuzhen Ding, Hongying Feng, Zhengliang Liu, Tianming Liu, William W. Wong, Sujay A. Vora, Jonathan B. Ashman, Wei Liu
Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.
1 code implementation • 21 Sep 2023 • Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Dingjie Song, Zhihong Chen, Abdulmohsen Alharthi, Bang An, Juncai He, Ziche Liu, Zhiyi Zhang, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
no code implementations • 18 Sep 2023 • Zhengliang Liu, Peilong Wang, Yiwei Li, Jason Holmes, Peng Shu, Lian Zhang, Chenbin Liu, Ninghao Liu, Dajiang Zhu, Xiang Li, Quanzheng Li, Samir H. Patel, Terence T. Sio, Tianming Liu, Wei Liu
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods.
no code implementations • 20 Jun 2023 • Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, Dajiang Zhu, Tianming Liu, Wei Liu
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
no code implementations • 1 Apr 2023 • Jason Holmes, Zhengliang Liu, Lian Zhang, Yuzhen Ding, Terence T. Sio, Lisa A. McGee, Jonathan B. Ashman, Xiang Li, Tianming Liu, Jiajian Shen, Wei Liu
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions.
no code implementations • 2 Feb 2023 • Jianqing Zhu, Juncai He, Lian Zhang, Jinchao Xu
By investigating iterative methods for a constrained linear model, we propose a new class of fully connected V-cycle MgNet for long-term time series forecasting, which is one of the most difficult tasks in forecasting.
no code implementations • 12 Jan 2022 • Yelu Gao, Huang Huang, Lian Zhang
Alzheimer's disease (AD) is an irreversible neurode generative disease of the brain. The disease may causes memory loss, difficulty communicating and disorientation.
no code implementations • 14 Dec 2021 • Juncai He, Jinchao Xu, Lian Zhang, Jianqing Zhu
We propose a constrained linear data-feature-mapping model as an interpretable mathematical model for image classification using a convolutional neural network (CNN).
1 code implementation • 23 Nov 2019 • Juncai He, Yuyan Chen, Lian Zhang, Jinchao Xu
In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet.
no code implementations • ICLR 2019 • Xiaodong Jia, Liang Zhao, Lian Zhang, Juncai He, Jinchao Xu
We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy.
no code implementations • 11 Jul 2018 • Juncai He, Xiaodong Jia, Jinchao Xu, Lian Zhang, Liang Zhao
Compressed Sensing using $\ell_1$ regularization is among the most powerful and popular sparsification technique in many applications, but why has it not been used to obtain sparse deep learning model such as convolutional neural network (CNN)?