Search Results for author: Bo Zhou

Found 29 papers, 10 papers with code

Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

1 code implementation14 Sep 2021 Haojie Shi, Bo Zhou, Hongsheng Zeng, Fan Wang, Yueqiang Dong, Jiangyong Li, Kang Wang, Hao Tian, Max Q. -H. Meng

However, due to the complex nonlinear dynamics in quadrupedal robots and reward sparsity, it is still difficult for RL to learn effective gaits from scratch, especially in challenging tasks such as walking over the balance beam.

ADER:Adapting between Exploration and Robustness for Actor-Critic Methods

no code implementations8 Sep 2021 Bo Zhou, Kejiao Li, Hongsheng Zeng, Fan Wang, Hao Tian

Combining off-policy reinforcement learning methods with function approximators such as neural networks has been found to lead to overestimation of the value function and sub-optimal solutions.

Continuous Control

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

1 code implementation12 Jul 2021 Bo Zhou, Chi Liu, James S. Duncan

The manual efforts can be alleviated if the manual segmentation in one imaging modality (e. g., CT) can be utilized to train a segmentation network in another imaging modality (e. g., CBCT/MRI/PET).

Contrastive Learning

Action Set Based Policy Optimization for Safe Power Grid Management

no code implementations29 Jun 2021 Bo Zhou, Hongsheng Zeng, Yuecheng Liu, Kejiao Li, Fan Wang, Hao Tian

At the planning stage, the search space is limited to the action set produced by the policy.

Decision Making

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

1 code implementation14 Apr 2021 Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan

Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Domain Adaptation Image Registration +1

Performance Analysis of Age of Information in Ultra-Dense Internet of Things (IoT) Systems with Noisy Channels

no code implementations9 Dec 2020 Bo Zhou, Walid Saad

Then, a mean-field approximation approach with guaranteed accuracy is developed to analyze the asymptotic performance for the considered system with an infinite number of devices and the effects of the system parameters on the average AoI are characterized.

Information Theory Networking and Internet Architecture Information Theory

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

no code implementations29 Nov 2020 Anton Smerdov, Andrey Somov, Evgeny Burnaev, Bo Zhou, Paul Lukowicz

In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.

Interpretable Machine Learning League of Legends +10

Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system

no code implementations9 Oct 2020 Jia-Yi Chang, Yan-Feng Lu, Ya-Jun Liu, Bo Zhou, Hong Qiao

In this model, we bring in multi-scale features from feature pyramid networks and make the features fu-sion to adjust prediction feature map of the output, which improves the sensitivity of the entire algorithm for tiny target faces.

Face Detection

Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning

no code implementations14 Sep 2020 Bo Zhou, Yu-Jung Tsai, Chi Liu

With high-quality recovered gated volumes, gate-to-gate motion vectors can be simultaneously outputted from the motion estimation network.

Denoising Motion Estimation +1

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

no code implementations3 Sep 2020 Bo Zhou, S. Kevin Zhou, James S. Duncan, Chi Liu

To derive quality reconstruction, previous state-of-the-art methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction.

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials

no code implementations5 Mar 2020 Antong Chen, Jennifer Saouaf, Bo Zhou, Randolph Crawford, Jianda Yuan, Junshui Ma, Richard Baumgartner, Shubing Wang, Gregory Goldmacher

Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials.

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

1 code implementation CVPR 2020 Bo Zhou, S. Kevin Zhou

In this work, we address the above two limitations by proposing a Dual Domain Recurrent Network (DuDoRNet) with deep T1 prior embedded to simultaneously recover k-space and images for accelerating the acquisition of MRI with a long imaging protocol.

MRI Reconstruction

Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion

no code implementations10 Dec 2019 Bo Zhou, Hongsheng Zeng, Fan Wang, Yunxiang Li, Hao Tian

By integrating dynamics models into model-free reinforcement learning (RL) methods, model-based value expansion (MVE) algorithms have shown a significant advantage in sample efficiency as well as value estimation.

Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning

no code implementations25 Sep 2019 Bo Zhou, Fan Wang, Hongsheng Zeng, Hao Tian

A promising direction is to combine model-based reinforcement learning with model-free reinforcement learning, such as model-based value expansion(MVE).

Model-based Reinforcement Learning

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

no code implementations5 Sep 2019 Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu

This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset.

A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions

no code implementations11 Nov 2018 Bo Zhou, Randolph Crawford, Belma Dogdas, Gregory Goldmacher, Antong Chen

For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements.

Lesion Segmentation

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network

1 code implementation22 Oct 2018 Bo Zhou, Xunyu Lin, Brendan Eck, Jun Hou, David L. Wilson

Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view.

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

1 code implementation3 Jul 2018 Bo Zhou, Yuemeng Li, Jiangcong Wang

We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography.

Classification General Classification

Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

no code implementations31 May 2018 Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang, Min Xu

The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept.

Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms

no code implementations31 Jan 2018 Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu

To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data.

Saliency Detection

Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction

no code implementations9 Sep 2017 Yanwei Pang, Bo Zhou, Feiping Nie

It is interesting that the optimal regularization parameter is adaptive to the neighbors in low-dimensional space and has intuitive meaning.

Supervised dimensionality reduction

Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection

no code implementations4 Jan 2017 Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki

Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing.

General Classification Transfer Learning

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