Search Results for author: Bo Zhou

Found 84 papers, 18 papers with code

Generating Temporally-ordered Event Sequences via Event Optimal Transport

no code implementations COLING 2022 Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, XiaoJian Jiang, Qiuxia Li

The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence.

Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach

no code implementations COLING 2022 Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, XiaoJian Jiang, Qiuxia Li

Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events.


LpQcM: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising

no code implementations27 Apr 2024 Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Georges EI Fakhri, Chi Liu

Specifically, the LpQcM consists of two components, the lesion-perceived modulation (LpM) and the multiscale quantification-consistent modulation (QcM).

Image Denoising

BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation

no code implementations24 Apr 2024 Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz

Once the student model is trained, the model only takes as inputs the BLE-RSSI data for inference, retaining the advantages of ubiquity and low cost of BLE RSSI.

Knowledge Distillation Position

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

no code implementations6 Apr 2024 Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou

To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step.

Image-to-Image Translation Translation

Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR

no code implementations22 Feb 2024 Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Lars Krupp, Vitor Fortes Rey, Paul Lukowicz

We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps.

Human Activity Recognition Quantization

iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition

no code implementations31 Jan 2024 Mengxi Liu, Vitor Fortes Rey, Yu Zhang, Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz

While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning. To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist. The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3. 22 \% reaching 84. 71 \% compared to the 81. 49 \% of the IMU baseline model.

Contrastive Learning Human Activity Recognition +1

Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

1 code implementation23 Jan 2024 Xiongchao Chen, Bo Zhou, Xueqi Guo, Huidong Xie, Qiong Liu, James S. Duncan, Albert J. Sinusas, Chi Liu

Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments.

Computed Tomography (CT) Denoising +1

Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey

no code implementations11 Jan 2024 Sizhen Bian, Mengxi Liu, Bo Zhou, Paul Lukowicz, Michele Magno

To this end, we first sorted the explorations into three domains according to the involved body forms: body-part electric field, whole-body electric field, and body-to-body electric field, and enumerated the state-of-art works in the domains with a detailed survey of the backed sensing tricks and targeted applications.

Human Activity Recognition

CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition

no code implementations3 Jan 2024 Mengxi Liu, Zimin Zhao, Daniel Geißler, Bo Zhou, Sungho Suh, Paul Lukowicz

Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors.

Human Activity Recognition Time Series

The Power of Training: How Different Neural Network Setups Influence the Energy Demand

no code implementations3 Jan 2024 Daniel Geißler, Bo Zhou, Mengxi Liu, Sungho Suh, Paul Lukowicz

This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective.

Transfer Learning

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

no code implementations7 Nov 2023 Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu

We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.

Image Denoising Medical Image Denoising

Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks

no code implementations29 Oct 2023 Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello, Bo Zhou, Mayank Shekhar Jha, Paul Lukowicz

The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works.

Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes

no code implementations16 Oct 2023 Yan Pan, Jiapeng Xie, Jiajie Wu, Bo Zhou

Although significant progress has been made, achieving place recognition in environments with perspective changes, seasonal variations, and scene transformations remains challenging.

TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction

1 code implementation23 Aug 2023 Xueqi Guo, Luyao Shi, Xiongchao Chen, Bo Zhou, Qiong Liu, Huidong Xie, Yi-Hwa Liu, Richard Palyo, Edward J. Miller, Albert J. Sinusas, Bruce Spottiswoode, Chi Liu, Nicha C. Dvornek

The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable.

Generative Adversarial Network Image Registration +1

Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries

no code implementations7 Aug 2023 Dhruv Mittal, Hymalai Bello, Bo Zhou, Mayank Shekhar Jha, Sungho Suh, Paul Lukowicz

Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications.


Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field

no code implementations7 Aug 2023 Sungho Suh, Vitor Fortes Rey, Sizhen Bian, Yu-Chi Huang, Jože M. Rožanec, Hooman Tavakoli Ghinani, Bo Zhou, Paul Lukowicz

This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line.

Activity Recognition Time Series

Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation

no code implementations26 Jul 2023 Long Liu, Bo Zhou, Zhipeng Zhao, Zening Liu

This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains.

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods

1 code implementation21 Jul 2023 Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz

To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap.


Real-Time Detection of Local No-Arbitrage Violations

no code implementations20 Jul 2023 Torben G. Andersen, Viktor Todorov, Bo Zhou

This paper focuses on the task of detecting local episodes involving violation of the standard It\^o semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities.

Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions

no code implementations18 Jul 2023 Huidong Xie, Bo Zhou, Xiongchao Chen, Xueqi Guo, Stephanie Thorn, Yi-Hwa Liu, Ge Wang, Albert Sinusas, Chi Liu

Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer.

Origami Single-end Capacitive Sensing for Continuous Shape Estimation of Morphing Structures

no code implementations3 Jul 2023 Lala Shakti Swarup Ray, Daniel Geißler, Bo Zhou, Paul Lukowicz, Berit Greinke

It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure.

MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities

no code implementations19 Jun 2023 Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz

The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective and ubiquitous monitoring systems.

Facial Expression Recognition Management +1

CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control

no code implementations7 Jun 2023 Hymalai Bello, Sungho Suh, Daniel Geißler, Lala Ray, Bo Zhou, Paul Lukowicz

We present CaptAinGlove, a textile-based, low-power (1. 15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control.

Hand Gesture Recognition Hand-Gesture Recognition +1

Skill-Based Few-Shot Selection for In-Context Learning

no code implementations23 May 2023 Shengnan An, Bo Zhou, Zeqi Lin, Qiang Fu, Bei Chen, Nanning Zheng, Weizhu Chen, Jian-Guang Lou

Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning.

In-Context Learning Semantic Parsing +1

WYWEB: A NLP Evaluation Benchmark For Classical Chinese

1 code implementation23 May 2023 Bo Zhou, Qianglong Chen, Tianyu Wang, Xiaomi Zhong, Yin Zhang

To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE.

Machine Translation Natural Language Understanding +2

FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors

no code implementations22 May 2023 Mengxi Liu, Bo Zhou, Zimin Zhao, Hyeonseok Hong, Hyun Kim, Sungho Suh, Vitor Fortes Rey, Paul Lukowicz

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration.

Human Activity Recognition

Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT

no code implementations17 May 2023 Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu

Additionally, computed tomography (CT)-derived attenuation maps ($\mu$-maps) are commonly used for SPECT attenuation correction (AC), but it will cause extra radiation exposure and SPECT-CT misalignments.

Computed Tomography (CT) Denoising

Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency

no code implementations17 May 2023 Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu

To overcome these challenges, we propose a dual-domain iterative network for end-to-end joint denoising and reconstruction from low-dose and few-angle projections of cardiac SPECT.


Semiparametrically Optimal Cointegration Test

no code implementations13 May 2023 Bo Zhou

By leveraging the structural version of LABF, an Ornstein-Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without a time trend.

MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features

1 code implementation12 May 2023 Bo Zhou, Jiapeng Xie, Yan Pan, Jiajie Wu, Chuanzhao Lu

In this paper, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentation, which segments moving objects with appearance and motion features in the bird's eye view (BEV) domain.

Collision Avoidance Computational Efficiency +2

Unified Noise-aware Network for Low-count PET Denoising

no code implementations28 Apr 2023 Huidong Xie, Qiong Liu, Bo Zhou, Xiongchao Chen, Xueqi Guo, Chi Liu

To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels.


Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System

no code implementations5 Apr 2023 Qihang Zhao, Rui-Jie Zhu, Liu Yang, He Yongming, Bo Zhou, Luo Cheng

In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking.

feature selection

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

1 code implementation2 Apr 2023 Bo Zhou, Huidong Xie, Qiong Liu, Xiongchao Chen, Xueqi Guo, Zhicheng Feng, Jun Hou, S. Kevin Zhou, Biao Li, Axel Rominger, Kuangyu Shi, James S. Duncan, Chi Liu

While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.

Denoising Personalized Federated Learning

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

no code implementations2 Mar 2023 Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang

Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.

Classification Decision Making +2

Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

no code implementations18 Feb 2023 Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, Michal Sofka

To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.

MRI Reconstruction Self-Supervised Learning

Frequency-Secured Unit Commitment: Tight Approximation using Bernstein Polynomials

no code implementations23 Dec 2022 Bo Zhou, Ruiwei Jiang, Siqian Shen

As we replace conventional synchronous generators with renewable energy, the frequency security of power systems is at higher risk.

Smart Cup: An impedance sensing based fluid intake monitoring system for beverages classification and freshness detection

no code implementations8 Oct 2022 Mengxi Liu, Sizhen Bian, Bo Zhou, Agnes Grünerbl, Paul Lukowicz

We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct beverages and the importance of features like amplitude, phase, and real and imaginary components for beverage classification.

Magnetic Field Based Hand Tracking

no code implementations18 Jul 2022 Sizhen Bian, Kexuan Guo, Mengxi Liu, Bo Zhou, Paul Lukowicz

In more detail, the transmitters generate the oscillating magnetic fields with a registered sequence, the receiver senses the strength of the induced magnetic field by a customized three axes coil, which is configured as the LC oscillator with the same oscillating frequency so that an induced current shows up when the receiver is located in the field of the generated magnetic field.

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

no code implementations13 Jun 2022 Xueqi Guo, Bo Zhou, David Pigg, Bruce Spottiswoode, Michael E. Casey, Chi Liu, Nicha C. Dvornek

The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information.

Motion Estimation

Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images

no code implementations28 May 2022 Kundan Sai Prabhu Thota, Sungho Suh, Bo Zhou, Paul Lukowicz

The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space.

Virtual Try-on

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.

reinforcement-learning Reinforcement Learning (RL)

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).

Anatomy Contrastive Learning +1

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.

Anatomy Domain Adaptation +3

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

1 code implementation29 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.

BIG-bench Machine Learning Interpretable Machine Learning +8

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.

reinforcement-learning Reinforcement Learning (RL)

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 reinforcement-learning +1

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 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 Segmentation

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

Cannot find the paper you are looking for? You can Submit a new open access paper.