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Medical image analysis

Nuclei Segmentation in Histopathology Images


Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming \textit{traditional} approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms.

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Authors:
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard
Submitted On:
16 May 2019 - 11:13am
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MercadierBesbinarFrossard_ICASSP2019_presentation.pdf

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[1] Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, "Nuclei Segmentation in Histopathology Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4539. Accessed: Aug. 19, 2019.
@article{4539-19,
url = {http://sigport.org/4539},
author = {Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard },
publisher = {IEEE SigPort},
title = {Nuclei Segmentation in Histopathology Images},
year = {2019} }
TY - EJOUR
T1 - Nuclei Segmentation in Histopathology Images
AU - Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4539
ER -
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). Nuclei Segmentation in Histopathology Images. IEEE SigPort. http://sigport.org/4539
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, 2019. Nuclei Segmentation in Histopathology Images. Available at: http://sigport.org/4539.
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). "Nuclei Segmentation in Histopathology Images." Web.
1. Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. Nuclei Segmentation in Histopathology Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4539

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.

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Authors:
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque
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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: Aug. 19, 2019.
@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

Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy


Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions & inherently high coherency of cancerous cells posing significant challenges to breast cancer (BC) multi-classification.

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Authors:
Sawon Pratiher, Subhankar Chattoraj
Submitted On:
11 May 2019 - 2:13am
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ICASSP_2104.pdf

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[1] Sawon Pratiher, Subhankar Chattoraj, "Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4436. Accessed: Aug. 19, 2019.
@article{4436-19,
url = {http://sigport.org/4436},
author = {Sawon Pratiher; Subhankar Chattoraj },
publisher = {IEEE SigPort},
title = {Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy},
year = {2019} }
TY - EJOUR
T1 - Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy
AU - Sawon Pratiher; Subhankar Chattoraj
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4436
ER -
Sawon Pratiher, Subhankar Chattoraj. (2019). Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy. IEEE SigPort. http://sigport.org/4436
Sawon Pratiher, Subhankar Chattoraj, 2019. Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy. Available at: http://sigport.org/4436.
Sawon Pratiher, Subhankar Chattoraj. (2019). "Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy." Web.
1. Sawon Pratiher, Subhankar Chattoraj. Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4436

UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE

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Authors:
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber
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10 May 2019 - 10:54am
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poster_icassp.pdf

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[1] Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber, "UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4350. Accessed: Aug. 19, 2019.
@article{4350-19,
url = {http://sigport.org/4350},
author = {Yanna Cavalcanti; Thomas Oberlin; Nicolas Dobigeon; Cédric Févotte; Simon Stute; Clovis Tauber },
publisher = {IEEE SigPort},
title = {UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE},
year = {2019} }
TY - EJOUR
T1 - UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE
AU - Yanna Cavalcanti; Thomas Oberlin; Nicolas Dobigeon; Cédric Févotte; Simon Stute; Clovis Tauber
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4350
ER -
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. (2019). UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE. IEEE SigPort. http://sigport.org/4350
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber, 2019. UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE. Available at: http://sigport.org/4350.
Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. (2019). "UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE." Web.
1. Yanna Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Clovis Tauber. UNMIXING DYNAMIC PET IMAGES: COMBINING SPATIAL HETEROGENEITY AND NON-GAUSSIAN NOISE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4350

Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection


Convolutional neural network (CNN) can be applied in glaucoma detection for achieving good performance.
However, its performance depends on the availability of a large number of the labelled samples for its training phase.
To solve this problem, this paper present a semi-supervised transfer learning CNN model for automatic glaucoma detection based on both labeled and unlabeled data.
First, a pre-trained CNN from non-medical data is fine-tuned and trained in a supervised fashion using the labeled data.

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Authors:
Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,
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9 May 2019 - 2:27pm
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[1] Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,, "Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4230. Accessed: Aug. 19, 2019.
@article{4230-19,
url = {http://sigport.org/4230},
author = {Manal AlGhamdi; Mingqi Li; Mohamed Abdel-Mottaleb; Mohamed Abou Shousha; },
publisher = {IEEE SigPort},
title = {Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection},
year = {2019} }
TY - EJOUR
T1 - Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection
AU - Manal AlGhamdi; Mingqi Li; Mohamed Abdel-Mottaleb; Mohamed Abou Shousha;
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4230
ER -
Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,. (2019). Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection. IEEE SigPort. http://sigport.org/4230
Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,, 2019. Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection. Available at: http://sigport.org/4230.
Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,. (2019). "Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection." Web.
1. Manal AlGhamdi, Mingqi Li, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha,. Semi-Supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4230

AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY


Human adapt their behaviors by continuously monitoring one another to function socially in our society. The ability to process face identity from memory is a crucial basic capability. In this work, we propose an event-contrastive connectome network (E-cCN) in representing brain’s functional connectivity with novel contrastive loss to handle layers of fMRI data variabilities exists under different controlled stimuli events to achieve improved automatic assessing of an individual’s face processing and memory ability.

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Authors:
Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee
Submitted On:
9 May 2019 - 12:41am
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Type:

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ICASSP2019_poster_fmri.pdf

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[1] Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee, "AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4147. Accessed: Aug. 19, 2019.
@article{4147-19,
url = {http://sigport.org/4147},
author = {Wan-Ting Hsieh; Hao-Chun Yang; Fu-Sheng Tsai; Chon-Wen Shyi; Chi-Chun Lee },
publisher = {IEEE SigPort},
title = {AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY},
year = {2019} }
TY - EJOUR
T1 - AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY
AU - Wan-Ting Hsieh; Hao-Chun Yang; Fu-Sheng Tsai; Chon-Wen Shyi; Chi-Chun Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4147
ER -
Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee. (2019). AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY. IEEE SigPort. http://sigport.org/4147
Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee, 2019. AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY. Available at: http://sigport.org/4147.
Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee. (2019). "AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY." Web.
1. Wan-Ting Hsieh, Hao-Chun Yang, Fu-Sheng Tsai, Chon-Wen Shyi, Chi-Chun Lee. AN EVENT-CONTRASTIVE CONNECTOME NETWORK FOR AUTOMATIC ASSESSMENT OF INDIVIDUAL FACE PROCESSING AND MEMORY ABILITY [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4147

SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS


Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment. While there are many computerised methods for skin lesion classification, convolutional neural networks (CNNs) have been shown to be superior over classical methods.

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Authors:
Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger
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8 May 2019 - 9:29am
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Skin Lesion Classification Using Hybrid deep Neural Networks.pdf

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[1] Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger, "SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4096. Accessed: Aug. 19, 2019.
@article{4096-19,
url = {http://sigport.org/4096},
author = {Amirreza Mahbod; Gerald Schaefer; Chunliang Wang; Rupert Ecker; Isabella Ellinger },
publisher = {IEEE SigPort},
title = {SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS
AU - Amirreza Mahbod; Gerald Schaefer; Chunliang Wang; Rupert Ecker; Isabella Ellinger
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4096
ER -
Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger. (2019). SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4096
Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger, 2019. SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS. Available at: http://sigport.org/4096.
Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger. (2019). "SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS." Web.
1. Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger. SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4096

POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS


Electroencephalography (EEG) has been widely used in human brain research. Several techniques in EEG relies on analyzing the topographical distribution of the data. One of the most common analysis is EEG microstate (EEG-ms). EEG-ms reflects the stable topographical representation of EEG signal lasting a few dozen milliseconds. EEG-ms were associated with resting state fMRI networks and were associated with mental processes and abnormalities.

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Authors:
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka
Submitted On:
23 November 2018 - 8:20pm
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A presentation for polarity invariant transformation for EEG microstates analysis

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[1] Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3761. Accessed: Aug. 19, 2019.
@article{3761-18,
url = {http://sigport.org/3761},
author = {Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka },
publisher = {IEEE SigPort},
title = {POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS
AU - Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3761
ER -
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. IEEE SigPort. http://sigport.org/3761
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , 2018. POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. Available at: http://sigport.org/3761.
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS." Web.
1. Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3761

HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?


Functional connectivity analysis by detecting neuronal coactivation in the brain can be efficiently done using Resting State Functional Magnetic Resonance Imaging (rs-fMRI) analysis. Most of the existing research in this area employ correlation-based group averaging strategies of spatial smoothing and temporal normalization of fMRI scans, whose reliability of results heavily depends on the voxel resolution of fMRI scan as well as scanning duration. Scanning period from 5 to 11 minutes has been chosen by most of the studies while estimating the connectivity of brain networks.

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Authors:
Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal
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22 November 2018 - 2:29pm
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[1] Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal, "HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3720. Accessed: Aug. 19, 2019.
@article{3720-18,
url = {http://sigport.org/3720},
author = {Debadatta Dash; Anil Kumar Sao; Jun Wang; Bharat Biswal },
publisher = {IEEE SigPort},
title = {HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?},
year = {2018} }
TY - EJOUR
T1 - HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?
AU - Debadatta Dash; Anil Kumar Sao; Jun Wang; Bharat Biswal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3720
ER -
Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal. (2018). HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?. IEEE SigPort. http://sigport.org/3720
Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal, 2018. HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?. Available at: http://sigport.org/3720.
Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal. (2018). "HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?." Web.
1. Debadatta Dash, Anil Kumar Sao, Jun Wang, Bharat Biswal. HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3720

SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS

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Authors:
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee
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10 October 2018 - 12:48am
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[1] Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee, "SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3658. Accessed: Aug. 19, 2019.
@article{3658-18,
url = {http://sigport.org/3658},
author = {Bijju Kranthi Veduruparthi; Jayanta Mukherjee; Partha Pratim Das; Mandira Saha; Sriram Prasath; Raj Kumar Shrimali; Soumendranath Ray; Sanjoy Chatterjee },
publisher = {IEEE SigPort},
title = {SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS},
year = {2018} }
TY - EJOUR
T1 - SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS
AU - Bijju Kranthi Veduruparthi; Jayanta Mukherjee; Partha Pratim Das; Mandira Saha; Sriram Prasath; Raj Kumar Shrimali; Soumendranath Ray; Sanjoy Chatterjee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3658
ER -
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. (2018). SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS. IEEE SigPort. http://sigport.org/3658
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee, 2018. SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS. Available at: http://sigport.org/3658.
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. (2018). "SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS." Web.
1. Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3658

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