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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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

Learning from the best: A teacher-student multilingual framework for low-resource languages


The traditional method of pretraining neural acoustic models in low-resource languages consists of initializing the acoustic model parameters with a large, annotated multilingual corpus and can be a drain on time and resources. In an attempt to reuse TDNN-LSTMs already pre-trained using multilingual training, we have applied Teacher-Student (TS) learning as a method of pretraining to transfer knowledge from a multilingual TDNN-LSTM to a TDNN. The pretraining time is reduced by an order of magnitude with the use of language-specific data during the teacher-student training.

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Authors:
Deblin Bagchi and William Hartmann
Submitted On:
13 May 2019 - 5:43pm
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[1] Deblin Bagchi and William Hartmann, "Learning from the best: A teacher-student multilingual framework for low-resource languages", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4493. Accessed: May. 30, 2020.
@article{4493-19,
url = {http://sigport.org/4493},
author = {Deblin Bagchi and William Hartmann },
publisher = {IEEE SigPort},
title = {Learning from the best: A teacher-student multilingual framework for low-resource languages},
year = {2019} }
TY - EJOUR
T1 - Learning from the best: A teacher-student multilingual framework for low-resource languages
AU - Deblin Bagchi and William Hartmann
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4493
ER -
Deblin Bagchi and William Hartmann. (2019). Learning from the best: A teacher-student multilingual framework for low-resource languages. IEEE SigPort. http://sigport.org/4493
Deblin Bagchi and William Hartmann, 2019. Learning from the best: A teacher-student multilingual framework for low-resource languages. Available at: http://sigport.org/4493.
Deblin Bagchi and William Hartmann. (2019). "Learning from the best: A teacher-student multilingual framework for low-resource languages." Web.
1. Deblin Bagchi and William Hartmann. Learning from the best: A teacher-student multilingual framework for low-resource languages [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4493

COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING

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Authors:
Fernando Merchan, Héctor Poveda, Eric Grivel
Submitted On:
13 May 2019 - 4:57pm
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[1] Fernando Merchan, Héctor Poveda, Eric Grivel , "COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4492. Accessed: May. 30, 2020.
@article{4492-19,
url = {http://sigport.org/4492},
author = {Fernando Merchan; Héctor Poveda; Eric Grivel },
publisher = {IEEE SigPort},
title = {COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING},
year = {2019} }
TY - EJOUR
T1 - COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING
AU - Fernando Merchan; Héctor Poveda; Eric Grivel
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4492
ER -
Fernando Merchan, Héctor Poveda, Eric Grivel . (2019). COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING. IEEE SigPort. http://sigport.org/4492
Fernando Merchan, Héctor Poveda, Eric Grivel , 2019. COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING. Available at: http://sigport.org/4492.
Fernando Merchan, Héctor Poveda, Eric Grivel . (2019). "COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING." Web.
1. Fernando Merchan, Héctor Poveda, Eric Grivel . COLLABORATION BETWEEN BORDEAUX-INP AND UTP, FROM RESEARCH TO EDUCATION, IN THE FIELD OF SIGNAL PROCESSING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4492

LoRa digital receiver analysis and implementation

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Authors:
Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg
Submitted On:
13 May 2019 - 1:47pm
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19ICASSP_Poster.pdf

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[1] Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg, "LoRa digital receiver analysis and implementation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4490. Accessed: May. 30, 2020.
@article{4490-19,
url = {http://sigport.org/4490},
author = {Reza Ghanaatian; Orion Afisiadis; Matthieu Cotting; Andreas Burg },
publisher = {IEEE SigPort},
title = {LoRa digital receiver analysis and implementation},
year = {2019} }
TY - EJOUR
T1 - LoRa digital receiver analysis and implementation
AU - Reza Ghanaatian; Orion Afisiadis; Matthieu Cotting; Andreas Burg
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4490
ER -
Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg. (2019). LoRa digital receiver analysis and implementation. IEEE SigPort. http://sigport.org/4490
Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg, 2019. LoRa digital receiver analysis and implementation. Available at: http://sigport.org/4490.
Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg. (2019). "LoRa digital receiver analysis and implementation." Web.
1. Reza Ghanaatian, Orion Afisiadis, Matthieu Cotting, Andreas Burg. LoRa digital receiver analysis and implementation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4490

Robust least squares estimation of graph signals


Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals.

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Authors:
Jari Miettinen, Sergiy Vorobyov, Esa Ollila
Submitted On:
13 May 2019 - 12:55pm
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[1] Jari Miettinen, Sergiy Vorobyov, Esa Ollila, "Robust least squares estimation of graph signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4489. Accessed: May. 30, 2020.
@article{4489-19,
url = {http://sigport.org/4489},
author = {Jari Miettinen; Sergiy Vorobyov; Esa Ollila },
publisher = {IEEE SigPort},
title = {Robust least squares estimation of graph signals},
year = {2019} }
TY - EJOUR
T1 - Robust least squares estimation of graph signals
AU - Jari Miettinen; Sergiy Vorobyov; Esa Ollila
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4489
ER -
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). Robust least squares estimation of graph signals. IEEE SigPort. http://sigport.org/4489
Jari Miettinen, Sergiy Vorobyov, Esa Ollila, 2019. Robust least squares estimation of graph signals. Available at: http://sigport.org/4489.
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). "Robust least squares estimation of graph signals." Web.
1. Jari Miettinen, Sergiy Vorobyov, Esa Ollila. Robust least squares estimation of graph signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4489

In-Car Driver Authentication Using Wireless Sensing


Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics.

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Authors:
Beibei Wang
Submitted On:
13 May 2019 - 11:17am
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[1] Beibei Wang, "In-Car Driver Authentication Using Wireless Sensing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4487. Accessed: May. 30, 2020.
@article{4487-19,
url = {http://sigport.org/4487},
author = {Beibei Wang },
publisher = {IEEE SigPort},
title = {In-Car Driver Authentication Using Wireless Sensing},
year = {2019} }
TY - EJOUR
T1 - In-Car Driver Authentication Using Wireless Sensing
AU - Beibei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4487
ER -
Beibei Wang. (2019). In-Car Driver Authentication Using Wireless Sensing. IEEE SigPort. http://sigport.org/4487
Beibei Wang, 2019. In-Car Driver Authentication Using Wireless Sensing. Available at: http://sigport.org/4487.
Beibei Wang. (2019). "In-Car Driver Authentication Using Wireless Sensing." Web.
1. Beibei Wang. In-Car Driver Authentication Using Wireless Sensing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4487

AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION


This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates.

Paper Details

Authors:
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
13 May 2019 - 9:23am
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[1] Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4486. Accessed: May. 30, 2020.
@article{4486-19,
url = {http://sigport.org/4486},
author = {Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION
AU - Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4486
ER -
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. IEEE SigPort. http://sigport.org/4486
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, 2019. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. Available at: http://sigport.org/4486.
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION." Web.
1. Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4486

Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks

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Authors:
Sumit Jha, Carlos Busso
Submitted On:
13 May 2019 - 9:22am
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[1] Sumit Jha, Carlos Busso, "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4485. Accessed: May. 30, 2020.
@article{4485-19,
url = {http://sigport.org/4485},
author = {Sumit Jha; Carlos Busso },
publisher = {IEEE SigPort},
title = {Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks
AU - Sumit Jha; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4485
ER -
Sumit Jha, Carlos Busso. (2019). Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4485
Sumit Jha, Carlos Busso, 2019. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. Available at: http://sigport.org/4485.
Sumit Jha, Carlos Busso. (2019). "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks." Web.
1. Sumit Jha, Carlos Busso. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4485

AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION


This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates.

Paper Details

Authors:
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
13 May 2019 - 9:23am
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Type:
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conference_poster_4.pdf

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[1] Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4484. Accessed: May. 30, 2020.
@article{4484-19,
url = {http://sigport.org/4484},
author = {Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION
AU - Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4484
ER -
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. IEEE SigPort. http://sigport.org/4484
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, 2019. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. Available at: http://sigport.org/4484.
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION." Web.
1. Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4484

wav2letter++ : A Fast Open-Source Speech Recognition Framework


This paper introduces wav2letter++, a fast open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition.

Paper Details

Authors:
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert
Submitted On:
13 May 2019 - 8:40am
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[1] Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, "wav2letter++ : A Fast Open-Source Speech Recognition Framework", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4483. Accessed: May. 30, 2020.
@article{4483-19,
url = {http://sigport.org/4483},
author = {Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert },
publisher = {IEEE SigPort},
title = {wav2letter++ : A Fast Open-Source Speech Recognition Framework},
year = {2019} }
TY - EJOUR
T1 - wav2letter++ : A Fast Open-Source Speech Recognition Framework
AU - Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4483
ER -
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). wav2letter++ : A Fast Open-Source Speech Recognition Framework. IEEE SigPort. http://sigport.org/4483
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, 2019. wav2letter++ : A Fast Open-Source Speech Recognition Framework. Available at: http://sigport.org/4483.
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). "wav2letter++ : A Fast Open-Source Speech Recognition Framework." Web.
1. Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. wav2letter++ : A Fast Open-Source Speech Recognition Framework [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4483

A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks


Lung cancer is the most prevalent cancer worldwide with about 230,000 new cases every year. Most cases go undiagnosed until it’s too late, especially in developing countries and remote areas. Early detection is key to beating cancer. Towards this end, the work presented here proposes an automated pipeline for lung tumor detection and segmentation from 3D lung CT scans from the NSCLC Radiomics Dataset. It also presents a new dilated hybrid-3D convolutional neural network architecture for tumor segmentation. First, a binary classifier chooses CT scan slices that may contain parts of a tumor.

Paper Details

Authors:
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque
Submitted On:
16 May 2019 - 8:05am
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LungNet3D-Poster

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[1] Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4482. Accessed: May. 30, 2020.
@article{4482-19,
url = {http://sigport.org/4482},
author = {Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque },
publisher = {IEEE SigPort},
title = {A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks
AU - Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4482
ER -
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4482
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, 2019. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. Available at: http://sigport.org/4482.
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks." Web.
1. Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4482

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