no code implementations • 13 Jun 2017 • Cunjing Ge, Feifei Ma, Tian Liu, Jian Zhang
Constrained counting is important in domains ranging from artificial intelligence to software analysis.
no code implementations • 26 Jun 2019 • Tian Liu, Tao Shu
Artificial neural network (ANN) provides superior accuracy for nonlinear alternating current (AC) state estimation (SE) in smart grid over traditional methods.
no code implementations • 17 Dec 2019 • Tian Liu, Li-Chun Wang, Shaofan Wang
Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling.
no code implementations • 27 Dec 2019 • Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.
no code implementations • 18 Jan 2020 • Tonghe Wang, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Xiaofeng Yang
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET).
no code implementations • 28 Jan 2020 • Yang Lei, Yabo Fu, Tonghe Wang, Richard L. J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
This paper presents a review of deep learning (DL) in multi-organ segmentation.
no code implementations • 28 Jun 2020 • Yunfei Song, Tian Liu, Tongquan Wei, Xiangfeng Wang, Zhe Tao, Mingsong Chen
Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications.
no code implementations • 8 Oct 2020 • Xianjin Dai, Yang Lei, Tonghe Wang, Anees H. Dhabaan, Mark McDonald, Jonathan J. Beitler, Walter J. Curran, Jun Zhou, Tian Liu, Xiaofeng Yang
The proposed method was evaluated on a cohort of 65 HN cancer patients.
Medical Physics Image and Video Processing
no code implementations • 25 Mar 2021 • Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
We summarize the latest AI-based methods for tumor subregion analysis and their applications.
no code implementations • 9 Aug 2021 • Shunbo Zhang, Shun Zhang, Jianpeng Ma, Tian Liu, Octavia A. Dobre
We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side.
no code implementations • 29 Nov 2021 • Tian Liu, Zhiwei Ling, Jun Xia, Xin Fu, Shui Yu, Mingsong Chen
Inspired by Knowledge Distillation (KD) that can increase the model accuracy, our approach adds the soft targets used by KD to the FL model training, which occupies negligible network resources.
no code implementations • 29 Jan 2022 • Tian Liu, Jiahao Ding, Ting Wang, Miao Pan, Mingsong Chen
However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.
no code implementations • 23 Feb 2022 • Ming Hu, Tian Liu, Zhiwei Ling, Zhihao Yue, Mingsong Chen
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy.
no code implementations • 28 Jun 2022 • Mingzhe Hu, Jiahan Zhang, Luke Matkovic, Tian Liu, Xiaofeng Yang
Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce.
no code implementations • 25 Jul 2022 • Tian Liu, Xueyang Hu, Tao Shu
Single-shot backdoor attack achieves high accuracy on both the main task and backdoor sub-task when injected at the FL model convergence.
no code implementations • 29 Aug 2022 • Huiqiao Xie, Yang Lei, Yabo Fu, Tonghe Wang, Justin Roper, Jeffrey D. Bradley, Pretesh Patel, Tian Liu, Xiaofeng Yang
The STN consists of a global generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to predict the coarse- and fine-scale motions, respectively.
no code implementations • 14 Sep 2022 • Yupei Zhang, Xianjin Dai, Zhen Tian, Yang Lei, Jacob F. Wynne, Pretesh Patel, Yue Chen, Tian Liu, Xiaofeng Yang
We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0. 94+/-0. 83 mm.
no code implementations • 25 Feb 2023 • Shaoyan Pan, Shao-Yuan Lo, Min Huang, Chaoqiong Ma, Jacob Wynne, Tonghe Wang, Tian Liu, Xiaofeng Yang
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
no code implementations • 30 Apr 2023 • Yuheng Li, Jacob Wynne, Jing Wang, Richard L. J. Qiu, Justin Roper, Shaoyan Pan, Ashesh B. Jani, Tian Liu, Pretesh R. Patel, Hui Mao, Xiaofeng Yang
We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework.
no code implementations • 28 Apr 2023 • Shaoyan Pan, Chih-Wei Chang, Junbo Peng, Jiahan Zhang, Richard L. J. Qiu, Tonghe Wang, Justin Roper, Tian Liu, Hui Mao, Xiaofeng Yang
The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities.
no code implementations • 17 Jan 2024 • Tian Liu, Yue Cui, Xueyang Hu, Yecheng Xu, Bo Liu
In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology.
no code implementations • 23 Jan 2024 • Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, Shu Kong
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
no code implementations • 29 Mar 2024 • Ting-Ting Zhu, Yuan-Hai Shao, Chun-Na Li, Tian Liu
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems.