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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION


Biomedical image segmentation has been widely studied, and lots of methods have been proposed. Among these methods, attention U-Net has achieved a promising performance. However, it has drawbacks of extracting the multi-scaled receptive field features at the high-level feature maps, resulting in the degeneration when dealing with the lesions with apparent scale variations. To solve this problem, this paper integrates an atrous spatial pyramid pooling (ASPP) module in the contracting path of attention U-Net.

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Authors:
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong
Submitted On:
12 February 2020 - 12:23pm
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Biomedical image segmentation

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[1] Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong, "A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4983. Accessed: Jul. 13, 2020.
@article{4983-20,
url = {http://sigport.org/4983},
author = {Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong },
publisher = {IEEE SigPort},
title = {A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION},
year = {2020} }
TY - EJOUR
T1 - A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION
AU - Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4983
ER -
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. (2020). A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/4983
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong, 2020. A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION. Available at: http://sigport.org/4983.
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. (2020). "A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION." Web.
1. Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4983

LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS


Head pose estimation from depth image is a challenging problem, considering its large pose variations, severer occlusions, and low quality of depth data. In contrast to existing approaches that take 2D depth image as input, we propose a novel deep regression architecture called Head PointNet, which consumes 3D point sets derived from a depth image describing the visible surface of a head. To cope with the non-stationary property of pose variation process, the network is facilitated with an ordinal regression module that incorporates metric penalties into ground truth label representation.

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Authors:
Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma
Submitted On:
12 February 2020 - 4:44am
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[1] Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma, "LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4981. Accessed: Jul. 13, 2020.
@article{4981-20,
url = {http://sigport.org/4981},
author = {Shihua Xiao; Nan Sang; Xupeng Wang; Xiangtian Ma },
publisher = {IEEE SigPort},
title = {LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS},
year = {2020} }
TY - EJOUR
T1 - LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS
AU - Shihua Xiao; Nan Sang; Xupeng Wang; Xiangtian Ma
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4981
ER -
Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma. (2020). LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS. IEEE SigPort. http://sigport.org/4981
Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma, 2020. LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS. Available at: http://sigport.org/4981.
Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma. (2020). "LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS." Web.
1. Shihua Xiao, Nan Sang, Xupeng Wang, Xiangtian Ma. LEVERAGING ORDINAL REGRESSION WITH SOFT LABELS FOR 3D HEAD POSE ESTIMATION FROM POINT SETS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4981

Multimodal active speaker detection and virtual cinematography for video conferencing


Active speaker detection (ASD) and virtual cinematography (VC) can significantly improve the remote user experience of a video conference by automatically panning, tilting and zooming of a video conferencing camera: users subjectively rate an expert video cinematographer’s video significantly higher than unedited video. We describe a new automated ASD and VC that performs within 0.3 MOS of an expert cinematographer based on subjective ratings with a 1-5 scale.

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Authors:
Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle
Submitted On:
12 February 2020 - 12:55am
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ICASSP 2020 ASD.pdf

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[1] Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle, "Multimodal active speaker detection and virtual cinematography for video conferencing", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4980. Accessed: Jul. 13, 2020.
@article{4980-20,
url = {http://sigport.org/4980},
author = {Ross Cutler; Ramin Mehran; Sam Johnson; Cha Zhang; Adam Kirk; Oliver Whyte; Adarsh Kowdle },
publisher = {IEEE SigPort},
title = {Multimodal active speaker detection and virtual cinematography for video conferencing},
year = {2020} }
TY - EJOUR
T1 - Multimodal active speaker detection and virtual cinematography for video conferencing
AU - Ross Cutler; Ramin Mehran; Sam Johnson; Cha Zhang; Adam Kirk; Oliver Whyte; Adarsh Kowdle
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4980
ER -
Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle. (2020). Multimodal active speaker detection and virtual cinematography for video conferencing. IEEE SigPort. http://sigport.org/4980
Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle, 2020. Multimodal active speaker detection and virtual cinematography for video conferencing. Available at: http://sigport.org/4980.
Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle. (2020). "Multimodal active speaker detection and virtual cinematography for video conferencing." Web.
1. Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle. Multimodal active speaker detection and virtual cinematography for video conferencing [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4980

Generalized Kernel-Based Dynamic Mode Decomposition

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Authors:
Patrick Héas, Cédric Herzet, Benoit Combès
Submitted On:
11 February 2020 - 8:21am
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[1] Patrick Héas, Cédric Herzet, Benoit Combès, "Generalized Kernel-Based Dynamic Mode Decomposition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4978. Accessed: Jul. 13, 2020.
@article{4978-20,
url = {http://sigport.org/4978},
author = {Patrick Héas; Cédric Herzet; Benoit Combès },
publisher = {IEEE SigPort},
title = {Generalized Kernel-Based Dynamic Mode Decomposition},
year = {2020} }
TY - EJOUR
T1 - Generalized Kernel-Based Dynamic Mode Decomposition
AU - Patrick Héas; Cédric Herzet; Benoit Combès
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4978
ER -
Patrick Héas, Cédric Herzet, Benoit Combès. (2020). Generalized Kernel-Based Dynamic Mode Decomposition. IEEE SigPort. http://sigport.org/4978
Patrick Héas, Cédric Herzet, Benoit Combès, 2020. Generalized Kernel-Based Dynamic Mode Decomposition. Available at: http://sigport.org/4978.
Patrick Héas, Cédric Herzet, Benoit Combès. (2020). "Generalized Kernel-Based Dynamic Mode Decomposition." Web.
1. Patrick Héas, Cédric Herzet, Benoit Combès. Generalized Kernel-Based Dynamic Mode Decomposition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4978

POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK


Capacity requirements of the fixed access network keep increasing towards multi-gigabit connections. For Hybrid Fiber Coaxial (HFC) networks, aggregated rates around 30 Gbit/s can be achieved by increasing the DOCSIS spectrum to 3GHz, assuming a spectral efficiency around 10 bit/s/Hz. Replacement of spectrum limiting components such as passive taps in the HFC network is an efficient way to achieve these data rates, compared with the cost of fiber to the home (FTTH).

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Authors:
Rainer Strobel, Thushara Hewavithana
Submitted On:
12 February 2020 - 3:25pm
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Coax_Architecture-FinalSubmit.pdf

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[1] Rainer Strobel, Thushara Hewavithana, "POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4977. Accessed: Jul. 13, 2020.
@article{4977-20,
url = {http://sigport.org/4977},
author = {Rainer Strobel; Thushara Hewavithana },
publisher = {IEEE SigPort},
title = {POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK},
year = {2020} }
TY - EJOUR
T1 - POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK
AU - Rainer Strobel; Thushara Hewavithana
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4977
ER -
Rainer Strobel, Thushara Hewavithana. (2020). POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK. IEEE SigPort. http://sigport.org/4977
Rainer Strobel, Thushara Hewavithana, 2020. POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK. Available at: http://sigport.org/4977.
Rainer Strobel, Thushara Hewavithana. (2020). "POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK." Web.
1. Rainer Strobel, Thushara Hewavithana. POWER SPECTRUM OPTIMIZATION FOR CAPACITY OF THE EXTENDED SPECTRUM HYBRID FIBER COAX NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4977

Counting dense objects in remote sensing images


Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness.

Paper Details

Authors:
Guangshuai Gao, Qingjie Liu, Yunhong Wang
Submitted On:
10 February 2020 - 11:09pm
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[1] Guangshuai Gao, Qingjie Liu, Yunhong Wang, "Counting dense objects in remote sensing images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4976. Accessed: Jul. 13, 2020.
@article{4976-20,
url = {http://sigport.org/4976},
author = {Guangshuai Gao; Qingjie Liu; Yunhong Wang },
publisher = {IEEE SigPort},
title = {Counting dense objects in remote sensing images},
year = {2020} }
TY - EJOUR
T1 - Counting dense objects in remote sensing images
AU - Guangshuai Gao; Qingjie Liu; Yunhong Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4976
ER -
Guangshuai Gao, Qingjie Liu, Yunhong Wang. (2020). Counting dense objects in remote sensing images. IEEE SigPort. http://sigport.org/4976
Guangshuai Gao, Qingjie Liu, Yunhong Wang, 2020. Counting dense objects in remote sensing images. Available at: http://sigport.org/4976.
Guangshuai Gao, Qingjie Liu, Yunhong Wang. (2020). "Counting dense objects in remote sensing images." Web.
1. Guangshuai Gao, Qingjie Liu, Yunhong Wang. Counting dense objects in remote sensing images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4976

Counting dense objects in remote sensing images


Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness.

Paper Details

Authors:
Guangshuai Gao, Qingjie Liu, Yunhong Wang
Submitted On:
10 February 2020 - 10:31pm
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[1] Guangshuai Gao, Qingjie Liu, Yunhong Wang, "Counting dense objects in remote sensing images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4975. Accessed: Jul. 13, 2020.
@article{4975-20,
url = {http://sigport.org/4975},
author = {Guangshuai Gao; Qingjie Liu; Yunhong Wang },
publisher = {IEEE SigPort},
title = {Counting dense objects in remote sensing images},
year = {2020} }
TY - EJOUR
T1 - Counting dense objects in remote sensing images
AU - Guangshuai Gao; Qingjie Liu; Yunhong Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4975
ER -
Guangshuai Gao, Qingjie Liu, Yunhong Wang. (2020). Counting dense objects in remote sensing images. IEEE SigPort. http://sigport.org/4975
Guangshuai Gao, Qingjie Liu, Yunhong Wang, 2020. Counting dense objects in remote sensing images. Available at: http://sigport.org/4975.
Guangshuai Gao, Qingjie Liu, Yunhong Wang. (2020). "Counting dense objects in remote sensing images." Web.
1. Guangshuai Gao, Qingjie Liu, Yunhong Wang. Counting dense objects in remote sensing images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4975

MoGA: Searching Beyond MobileNetV3


In this paper, we aim to bring forward the frontier of mobile neural architecture design by utilizing the latest neural architecture search (NAS) approaches. First, we shift the search trend from mobile CPUs to mobile GPUs, with which we can gauge the speed of a model more accurately and provide a production-ready solution. On this account, our overall search approach is named \alert{Mobile GPU-Aware neural architecture search (MoGA)}.

Paper Details

Authors:
Xiangxiang Chu, Bo Zhang, Ruijun Xu
Submitted On:
10 February 2020 - 11:33am
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Poster to ICASSP 2020 Paper - MoGA: Searching Beyond MobileNetV3

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[1] Xiangxiang Chu, Bo Zhang, Ruijun Xu, "MoGA: Searching Beyond MobileNetV3", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4974. Accessed: Jul. 13, 2020.
@article{4974-20,
url = {http://sigport.org/4974},
author = {Xiangxiang Chu; Bo Zhang; Ruijun Xu },
publisher = {IEEE SigPort},
title = {MoGA: Searching Beyond MobileNetV3},
year = {2020} }
TY - EJOUR
T1 - MoGA: Searching Beyond MobileNetV3
AU - Xiangxiang Chu; Bo Zhang; Ruijun Xu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4974
ER -
Xiangxiang Chu, Bo Zhang, Ruijun Xu. (2020). MoGA: Searching Beyond MobileNetV3. IEEE SigPort. http://sigport.org/4974
Xiangxiang Chu, Bo Zhang, Ruijun Xu, 2020. MoGA: Searching Beyond MobileNetV3. Available at: http://sigport.org/4974.
Xiangxiang Chu, Bo Zhang, Ruijun Xu. (2020). "MoGA: Searching Beyond MobileNetV3." Web.
1. Xiangxiang Chu, Bo Zhang, Ruijun Xu. MoGA: Searching Beyond MobileNetV3 [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4974

A whiteness test based on the spectral measure of large non-Hermitian random matrices


In the context of multivariate time series, a whiteness test against an MA(1)
correlation model is proposed. This test is built on the eigenvalue
distribution (spectral measure) of the non-Hermitian one-lag sample
autocovariance matrix, instead of its singular value distribution. The large
dimensional limit spectral measure of this matrix is derived. To obtain this
result, a control over the smallest singular value of a related random matrix
is provided. Numerical simulations show the excellent performance of this
test.

Paper Details

Authors:
Arup Bose, Walid Hachem
Submitted On:
10 February 2020 - 4:17am
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Bose/Hachem ICASSP paper

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[1] Arup Bose, Walid Hachem, "A whiteness test based on the spectral measure of large non-Hermitian random matrices", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4973. Accessed: Jul. 13, 2020.
@article{4973-20,
url = {http://sigport.org/4973},
author = {Arup Bose; Walid Hachem },
publisher = {IEEE SigPort},
title = {A whiteness test based on the spectral measure of large non-Hermitian random matrices},
year = {2020} }
TY - EJOUR
T1 - A whiteness test based on the spectral measure of large non-Hermitian random matrices
AU - Arup Bose; Walid Hachem
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4973
ER -
Arup Bose, Walid Hachem. (2020). A whiteness test based on the spectral measure of large non-Hermitian random matrices. IEEE SigPort. http://sigport.org/4973
Arup Bose, Walid Hachem, 2020. A whiteness test based on the spectral measure of large non-Hermitian random matrices. Available at: http://sigport.org/4973.
Arup Bose, Walid Hachem. (2020). "A whiteness test based on the spectral measure of large non-Hermitian random matrices." Web.
1. Arup Bose, Walid Hachem. A whiteness test based on the spectral measure of large non-Hermitian random matrices [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4973

Lightweight V-Net for Liver Segmentation


The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To address these issues, we design a lightweight V-Net (LV-Net) for liver segmentation in this paper. The proposed network makes two contributions. The first is that we design an inverted residual bottleneck block (IRB block) and a 3D average pooling block and apply them to the proposed LV-Net.

Paper Details

Authors:
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi
Submitted On:
6 February 2020 - 5:47am
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[1] Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi, "Lightweight V-Net for Liver Segmentation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4972. Accessed: Jul. 13, 2020.
@article{4972-20,
url = {http://sigport.org/4972},
author = {Wenzheng Zhou; Yuxiao Zhang;Risheng Wang; Hongying Meng; asoke K. Nandi },
publisher = {IEEE SigPort},
title = {Lightweight V-Net for Liver Segmentation},
year = {2020} }
TY - EJOUR
T1 - Lightweight V-Net for Liver Segmentation
AU - Wenzheng Zhou; Yuxiao Zhang;Risheng Wang; Hongying Meng; asoke K. Nandi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4972
ER -
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. (2020). Lightweight V-Net for Liver Segmentation. IEEE SigPort. http://sigport.org/4972
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi, 2020. Lightweight V-Net for Liver Segmentation. Available at: http://sigport.org/4972.
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. (2020). "Lightweight V-Net for Liver Segmentation." Web.
1. Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. Lightweight V-Net for Liver Segmentation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4972

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