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Xinzhi Wang - IEEE Xplore Author Profile

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Author details

Xinzhi Wang

Publications

18

Citations

90

Publications by Year

20132024

Co-Authors:

Yudong ChangJinjun ChenMingke GaoJiayu GuoChunning Hou

Show All Co-Authors (38)

Xinzhi Wang

Affiliation

School of Computer Engineering and Science

Shanghai University

Shanghai, China

Publication Topics

Attention Mechanism,
Attention Scores,
F1 Score,
Graph Neural Networks,
Implicit Features,
Model Performance,
Ablation Experiments,
Action-value Function,
Actual Values,
Aspect-based Sentiment Analysis,
Aspects Of Representation,
Attention Module

Biography

Xinzhi Wang is currently an Assistant Professor with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. She ever visited Carnegie Mellon University (CMU) for one year in 2017–2018. Her recently researches focus on natural language and image processing, including sentiment analysis, AI transparency, agent action planning, and agent action intervention. She received the Ph.D. degree from Tsinghua University, Beijing, China, in 2019. She is the corresponding author. Contact her at wxz2017@shu.edu.cn.(Based on document published on 6 April 2021).

Publications

18

Citations

90

Publications by Year

20132024

Co-Authors:

Yudong Chang
Jinjun Chen
Mingke Gao
Jiayu Guo
Chunning Hou

Show All Co-Authors (38)

Author's Published Works

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Showing 1-9 of 9 results

Conferences (6)

Journals (3)

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Ganapathy Krishnamurthi(8)
Varghese Alex Kollerathu(2)
S. Vinitha Sree(2)
Mahendra Khened(2)
Kanti Kumari(2)
Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India(2)
Department of Science of the Images, Policlinico Universitario, Cagliari, Italy(1)
School of Medicine, Department of Pediatric-Endocrinology, Indiana University, Indianapolis, IN, USA(1)
Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada(1)
Inria-Asclepios Project, Sophia Antipolis, France(1)
IEEE Transactions on Medical Imaging(2)
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society(1)
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)(1)
2019 IEEE International Ultrasonics Symposium (IUS)(1)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(1)
IEEE(9)
Boston, MA, USA(1)
Cartagena, Colombia(1)
Glasgow, UK(1)
Long Beach, CA, USA(1)
Singapore(1)
Convolutional Neural Network(3)
Neural Network(3)
Segmentation Results(3)
Ultrasound Imaging(3)
3D U-Net(2)

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Results

Parse Challenge 2022: Pulmonary Arteries Segmentation Using Swin U-net Transformer(Swin UNETR) and U-net

Rohan Padhy;Akansh Maurya;Kunal Dasharath Patil;Kalluri Ramakrishna;Ganapathy Krishnamurthi

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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In this paper, we describe a deep neural network architecture based on Swin UNETR and U-Net for segmenting the pulmonary arteries from CT scans. The final segmentation masks were created using an ensemble of six models, three based on Swin UNETR and three based on 3D U-net with residual units. Using this strategy, our group scored 84.36 % on the multi-level dice. We conducted additional investigat...Show More

Parse Challenge 2022: Pulmonary Arteries Segmentation Using Swin U-net Transformer(Swin UNETR) and U-net

Rohan Padhy;Akansh Maurya;Kunal Dasharath Patil;Kalluri Ramakrishna;Ganapathy Krishnamurthi

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

Year: 2023 | Conference Paper |

GAN-based End-to-End Unsupervised Image Registration for RGB-Infrared Image

Kanti Kumari;Ganapathy Krishnamurthi

2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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Image registration is a pre-processing step used in various computer vision applications. This paper presents an unsupervised image registration for a given pair of RGB and infrared images, with the RGB image used as the reference for infrared. This method exploits the GAN architecture with a spatial transformer module, to synthesize the transformed image using an unsupervised loss criterion. This...Show More

GAN-based End-to-End Unsupervised Image Registration for RGB-Infrared Image

Kanti Kumari;Ganapathy Krishnamurthi

2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS)

Year: 2020 | Conference Paper |

NTIRE 2019 Challenge on Image Enhancement: Methods and Results

Andrey Ignatov;Radu Timofte;Xiaochao Qu;Xingguang Zhou;Ting Liu;Pengfei Wan;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Dongwon Park;Se Young Chun;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Zhiwei Zhong;Xianming Liu;Junjun Jiang;Debin Zhao;Muhammad Haris;Kazutoshi Akita;Tomoki Yoshida;Greg Shakhnarovich;Norimichi Ukita;Jie Liu;Cheolkon Jung;Raimondo Schettini;Simone Bianco;Claudio Cusano;Flavio Piccoli;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Nelson Chong Ngee Bow;Lai-Kuan Wong;John See;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Zheng Hui;Xiumei Wang;Xinbo Gao;Kanti Kumari;Vikas Kumar Anand;Mahendra Khened;Ganapathy Krishnamurthi

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

Cited by: Papers (29)

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This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks...Show More

NTIRE 2019 Challenge on Image Enhancement: Methods and Results

Andrey Ignatov;Radu Timofte;Xiaochao Qu;Xingguang Zhou;Ting Liu;Pengfei Wan;Syed Waqas Zamir;Aditya Arora;Salman Khan;Fahad Shahbaz Khan;Ling Shao;Dongwon Park;Se Young Chun;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Zhiwei Zhong;Xianming Liu;Junjun Jiang;Debin Zhao;Muhammad Haris;Kazutoshi Akita;Tomoki Yoshida;Greg Shakhnarovich;Norimichi Ukita;Jie Liu;Cheolkon Jung;Raimondo Schettini;Simone Bianco;Claudio Cusano;Flavio Piccoli;Pengju Liu;Kai Zhang;Jingdong Liu;Jiye Liu;Hongzhi Zhang;Wangmeng Zuo;Nelson Chong Ngee Bow;Lai-Kuan Wong;John See;Jinghui Qin;Lishan Huang;Yukai Shi;Pengxu Wei;Wushao Wen;Liang Lin;Zheng Hui;Xiumei Wang;Xinbo Gao;Kanti Kumari;Vikas Kumar Anand;Mahendra Khened;Ganapathy Krishnamurthi

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2019 | Conference Paper |

Deep-Learning based Identification of Frames Containing Foetal Gender Region During Early Second Trimester Ultrasound Scanning

Praveen Paul Lakra;Ashish Kumar;Narayanaswamy Mohanram;Ganapathy Krishnamurthi;Arun Kumar Thittai

2019 IEEE International Ultrasonics Symposium (IUS)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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Sex-selective abortions, based on ultrasound foetal scanning of pregnant women, is a rampant problem in developing countries like India. Although prenatal gender screening has been outlawed, poor enforcement of it has abetted a steady rise in their number. Currently, the B-scans are displayed on the screen in real-time and the operator can make out the gender from the displayed image. Therefore, e...Show More

Deep-Learning based Identification of Frames Containing Foetal Gender Region During Early Second Trimester Ultrasound Scanning

Praveen Paul Lakra;Ashish Kumar;Narayanaswamy Mohanram;Ganapathy Krishnamurthi;Arun Kumar Thittai

2019 IEEE International Ultrasonics Symposium (IUS)

Year: 2019 | Conference Paper |

Recurrent Attention Mechanism Networks for Enhanced Classification of Biomedical Images

Mazhar Shaikh;Varghese Alex Kollerathu;Ganapathy Krishnamurthi

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Year: 2019 | Conference Paper |

Cited by: Papers (9)

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Convolutional neural networks achieve state of the art results for a variety of tasks. However, this improved performance comes at the cost of performing convolutional operations throughout the entire image. Resizing of images to manageable levels is one of the often used techniques so as to reduce this computational overhead. On medical images, lesions are represented by a minuscule proportion of...Show More

Recurrent Attention Mechanism Networks for Enhanced Classification of Biomedical Images

Mazhar Shaikh;Varghese Alex Kollerathu;Ganapathy Krishnamurthi

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Year: 2019 | Conference Paper |

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Cited by: Papers (1176)

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Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large...Show More

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration

Filippo Molinari;Ganapathy Krishnamurthi;U. Rajendra Acharya;S. Vinitha Sree;Guang Zeng;Luca Saba;Andrew Nicolaides;Jasjit S. Suri

IEEE Transactions on Instrumentation and Measurement

Year: 2012 | Volume: 61, Issue: 4 | Journal Article |

Cited by: Papers (39)

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Intima-media thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as the Completely Automated Layer EXtraction (CALEX) (which is a class of patented AtheroEdge Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities a...Show More

Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration

Filippo Molinari;Ganapathy Krishnamurthi;U. Rajendra Acharya;S. Vinitha Sree;Guang Zeng;Luca Saba;Andrew Nicolaides;Jasjit S. Suri

IEEE Transactions on Instrumentation and Measurement

Year: 2012 | Volume: 61, Issue: 4 | Journal Article |

Atheromatic™: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture

U Rajendra Acharya;Oliver Faust;S. Vinitha Sree;Ang Peng Chuan Alvin;Ganapathy Krishnamurthi;Jos´e C. R. Seabra;João Sanches;Jasjit S Suri

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Cited by: Papers (19)

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Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies ...Show More

Atheromatic™: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture

U Rajendra Acharya;Oliver Faust;S. Vinitha Sree;Ang Peng Chuan Alvin;Ganapathy Krishnamurthi;Jos´e C. R. Seabra;João Sanches;Jasjit S Suri

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Functional imaging in small animals using X-ray computed Tomography -study of physiologic measurement reproducibility

G. Krishnamurthi;K.M. Stantz;R. Steinmetz;V.H. Gattone;Minsong Cao;G.D. Hutchins;Yun Liang

IEEE Transactions on Medical Imaging

Year: 2005 | Volume: 24, Issue: 7 | Journal Article |

Cited by: Papers (17)

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X-ray computed tomography (CT) has been traditionally used for morphologic analysis and in the recent past has been used for physiology imaging. This paper seeks to demonstrate functional CT as an effective tool for monitoring changes in tissue physiology associated with disease processes and cellular and molecular level therapeutic processes. We investigated the effect of noise and sampling time ...Show More

Functional imaging in small animals using X-ray computed Tomography -study of physiologic measurement reproducibility

G. Krishnamurthi;K.M. Stantz;R. Steinmetz;V.H. Gattone;Minsong Cao;G.D. Hutchins;Yun Liang

IEEE Transactions on Medical Imaging

Year: 2005 | Volume: 24, Issue: 7 | Journal Article |

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