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Biomedical signal processing

RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION

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Authors:
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf
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20 March 2017 - 12:25pm
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[1] Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf, "RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1778. Accessed: Jul. 22, 2017.
@article{1778-17,
url = {http://sigport.org/1778},
author = {Elmar Messner; Martin Hagmüller; Paul Swatek; Freyja-Maria Smolle-Jüttner; Franz Pernkopf },
publisher = {IEEE SigPort},
title = {RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION},
year = {2017} }
TY - EJOUR
T1 - RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION
AU - Elmar Messner; Martin Hagmüller; Paul Swatek; Freyja-Maria Smolle-Jüttner; Franz Pernkopf
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1778
ER -
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. (2017). RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION. IEEE SigPort. http://sigport.org/1778
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf, 2017. RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION. Available at: http://sigport.org/1778.
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. (2017). "RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION." Web.
1. Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1778

Non-Convex Sparse Optimization for Photon-Limited Imaging


While convex optimization for low-light imaging has received some attention by the imaging community, non-convex optimization techniques for photon-limited imaging are still in their nascent stages. In this thesis, we developed a stage-based non-convex approach to recover high-resolution sparse signals from low-dimensional measurements corrupted by Poisson noise. We incorporate gradient-based information to construct a sequence of quadratic subproblems with an $\ell_p$-norm ($0 \leq p < 1$) penalty term to promote sparsity.

PhDForum.pdf

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Authors:
Lasith Adhikari, Roummel Marcia
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6 March 2017 - 10:40am
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PhDForum.pdf

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[1] Lasith Adhikari, Roummel Marcia, "Non-Convex Sparse Optimization for Photon-Limited Imaging", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1650. Accessed: Jul. 22, 2017.
@article{1650-17,
url = {http://sigport.org/1650},
author = {Lasith Adhikari; Roummel Marcia },
publisher = {IEEE SigPort},
title = {Non-Convex Sparse Optimization for Photon-Limited Imaging},
year = {2017} }
TY - EJOUR
T1 - Non-Convex Sparse Optimization for Photon-Limited Imaging
AU - Lasith Adhikari; Roummel Marcia
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1650
ER -
Lasith Adhikari, Roummel Marcia. (2017). Non-Convex Sparse Optimization for Photon-Limited Imaging. IEEE SigPort. http://sigport.org/1650
Lasith Adhikari, Roummel Marcia, 2017. Non-Convex Sparse Optimization for Photon-Limited Imaging. Available at: http://sigport.org/1650.
Lasith Adhikari, Roummel Marcia. (2017). "Non-Convex Sparse Optimization for Photon-Limited Imaging." Web.
1. Lasith Adhikari, Roummel Marcia. Non-Convex Sparse Optimization for Photon-Limited Imaging [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1650

Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework


The sampling of neural signals plays an important role in modern neuroscience, especially for prosthetics. However, due to hardware and data rate constraints, only spike trains can get recovered reliably. State of the art prosthetics can still achieve impressive results, but to get higher resolutions the used data rate needs to be reduced. In this paper, this is done by expressing the data with exponential and sinusoidal splines.

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3 March 2017 - 6:30am
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FRIVortrag.pdf

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FRIVortrag.pdf

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[1] , "Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1605. Accessed: Jul. 22, 2017.
@article{1605-17,
url = {http://sigport.org/1605},
author = { },
publisher = {IEEE SigPort},
title = {Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework},
year = {2017} }
TY - EJOUR
T1 - Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1605
ER -
. (2017). Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework. IEEE SigPort. http://sigport.org/1605
, 2017. Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework. Available at: http://sigport.org/1605.
. (2017). "Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework." Web.
1. . Reduction of Necessary Data Rate for Neural Data Through Exponential and Sinusoidal Spline Decomposition using the Finite Rate of Innovation Framework [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1605

EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE


In this study we investigate whether or not event-related (de)synchronisation (ERD/ERS) can be used to differenti- ate between 27 healthy elderly, 21 subjects diagnosed with amnestic mild cognitive impairment (aMCI) and 16 mild Alzheimer’s disease (AD) patients. Using 32-channel EEG recordings, we measured ERD responses to a three-level vi- sual N-back task (N = 0, 1, 2) on the well-known delta, theta, alpha, beta and gamma bands.

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Authors:
Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips
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2 March 2017 - 7:08am
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ICASSP 2017 Paper #3050 (Francisco J Fraga) - ERD responses to N-back tasks discriminate MCI from Healthy and AD patients

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[1] Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips, "EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1582. Accessed: Jul. 22, 2017.
@article{1582-17,
url = {http://sigport.org/1582},
author = {Francisco J. Fraga; Leonardo A. Ferreira; Tiago H. Falk; Erin Johns; Natalie D. Phillips },
publisher = {IEEE SigPort},
title = {EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE},
year = {2017} }
TY - EJOUR
T1 - EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE
AU - Francisco J. Fraga; Leonardo A. Ferreira; Tiago H. Falk; Erin Johns; Natalie D. Phillips
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1582
ER -
Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips. (2017). EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE. IEEE SigPort. http://sigport.org/1582
Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips, 2017. EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE. Available at: http://sigport.org/1582.
Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips. (2017). "EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE." Web.
1. Francisco J. Fraga, Leonardo A. Ferreira, Tiago H. Falk, Erin Johns, Natalie D. Phillips. EVENT-RELATED SYNCHRONISATION RESPONSES TO N-BACK MEMORY TASKS DISCRIMINATE BETWEEN HEALTHY AGEING, MILD COGNITIVE IMPAIRMENT, AND MILD ALZHEIMER’S DISEASE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1582

COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION


Atrial fibrillation (AF) patients need long-term electrocardiography (ECG) monitoring to detect occurrence of AF. We can acquire ECG signals under low power by compressive sensing based sensor and detect AF by existing algorithms. However, the compression ratio of AF signal is low when DWT basis is applied for CS reconstruction. On the other hand the complexity of AF detection algorithms is high. In this paper, we propose a CS-based ECG monitoring system with effective AF detection. We exploit dictionary learning to improve 2.5x better compression ratio than existing works.

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Authors:
Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu
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1 March 2017 - 1:50am
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[1] Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu, " COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1534. Accessed: Jul. 22, 2017.
@article{1534-17,
url = {http://sigport.org/1534},
author = {Hung-Chi Kuo; Yu-Min Lin and An-Yeu (Andy) Wu },
publisher = {IEEE SigPort},
title = { COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION},
year = {2017} }
TY - EJOUR
T1 - COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION
AU - Hung-Chi Kuo; Yu-Min Lin and An-Yeu (Andy) Wu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1534
ER -
Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu. (2017). COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION. IEEE SigPort. http://sigport.org/1534
Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu, 2017. COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION. Available at: http://sigport.org/1534.
Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu. (2017). " COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION." Web.
1. Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu. COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1534

EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER


In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class,the classification accuracy is guaranteed by a constraint that the ratio
of variances of signals in two different classes is lower bounded.Then, a sparse spatial filter is achieved by minimizing the l1-norm of filter coefficients and channels of EEG signals can be further optimized.

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11 March 2017 - 8:49pm
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Poster-ICASSP.pdf

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[1] , "EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1518. Accessed: Jul. 22, 2017.
@article{1518-17,
url = {http://sigport.org/1518},
author = { },
publisher = {IEEE SigPort},
title = {EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER},
year = {2017} }
TY - EJOUR
T1 - EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1518
ER -
. (2017). EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER. IEEE SigPort. http://sigport.org/1518
, 2017. EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER. Available at: http://sigport.org/1518.
. (2017). "EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER." Web.
1. . EEG CHANNEL OPTIMIZATION VIA SPARSE COMMON SPATIAL FILTER [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1518

Fast and Stable Signal Deconvolution via Compressible State-Space Models


Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete

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Authors:
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi
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12 December 2016 - 9:35am
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[1] Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, "Fast and Stable Signal Deconvolution via Compressible State-Space Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1438. Accessed: Jul. 22, 2017.
@article{1438-16,
url = {http://sigport.org/1438},
author = {Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Fast and Stable Signal Deconvolution via Compressible State-Space Models},
year = {2016} }
TY - EJOUR
T1 - Fast and Stable Signal Deconvolution via Compressible State-Space Models
AU - Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1438
ER -
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). Fast and Stable Signal Deconvolution via Compressible State-Space Models. IEEE SigPort. http://sigport.org/1438
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, 2016. Fast and Stable Signal Deconvolution via Compressible State-Space Models. Available at: http://sigport.org/1438.
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). "Fast and Stable Signal Deconvolution via Compressible State-Space Models." Web.
1. Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. Fast and Stable Signal Deconvolution via Compressible State-Space Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1438

COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS


Cancers originating from different organs can show similar genomic alterations whereas cancers originating from the same organ can vary across patients. Therefore cancer stratification that does not depend on the tissue of the origin can play an important role to better understand cancers having similar genomic patterns irrespective of their origins. In this work, we formulated the problem as a weighted graph and communities were found using a modularity maximization based graph clustering method. We classified 3,199 subjects from twelve different cancer types into five clusters.

GlobalSIP.pdf

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Authors:
Nandinee Haq, Z. Jane Wang
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8 December 2016 - 2:41pm
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[1] Nandinee Haq, Z. Jane Wang, "COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1424. Accessed: Jul. 22, 2017.
@article{1424-16,
url = {http://sigport.org/1424},
author = {Nandinee Haq; Z. Jane Wang },
publisher = {IEEE SigPort},
title = {COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS},
year = {2016} }
TY - EJOUR
T1 - COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS
AU - Nandinee Haq; Z. Jane Wang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1424
ER -
Nandinee Haq, Z. Jane Wang. (2016). COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS. IEEE SigPort. http://sigport.org/1424
Nandinee Haq, Z. Jane Wang, 2016. COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS. Available at: http://sigport.org/1424.
Nandinee Haq, Z. Jane Wang. (2016). "COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS." Web.
1. Nandinee Haq, Z. Jane Wang. COMMUNITY DETECTION FROM GENOMIC DATASETS ACROSS HUMAN CANCERS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1424

Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects


This paper provides a theoretical analysis on the classification accuracy of LDA-Bayesian based method with respect

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Authors:
Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li
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6 December 2016 - 12:51pm
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[1] Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li, "Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1371. Accessed: Jul. 22, 2017.
@article{1371-16,
url = {http://sigport.org/1371},
author = {Zhe Wang; Tianlong Song; Yuan Liang; Tongtong Li },
publisher = {IEEE SigPort},
title = {Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects},
year = {2016} }
TY - EJOUR
T1 - Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects
AU - Zhe Wang; Tianlong Song; Yuan Liang; Tongtong Li
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1371
ER -
Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li. (2016). Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects. IEEE SigPort. http://sigport.org/1371
Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li, 2016. Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects. Available at: http://sigport.org/1371.
Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li. (2016). "Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects." Web.
1. Zhe Wang, Tianlong Song, Yuan Liang, Tongtong Li. Error Probability Analysis for LDA-Bayesian Based Classification of Alzheimer's Disease and Normal Control Subjects [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1371

PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy


In this dissertation, we propose the first, to the best of our knowledge, PCA based algorithm to noninvasively recognize and classify different temporal stages of brain tumors given a large time series of MRI images. We propose an algorithm that addresses the challenging task of classifying stage of tumor over period of time while the tumor is being treated with VB-111 virotherapy. Our approach treats stage tumor recognition as a two-dimensional recognition problem.

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16 November 2016 - 9:38am
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PCA based Algorithm for Longitudinal Brain Tumor Stage .pptx

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[1] , "PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1267. Accessed: Jul. 22, 2017.
@article{1267-16,
url = {http://sigport.org/1267},
author = { },
publisher = {IEEE SigPort},
title = {PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy },
year = {2016} }
TY - EJOUR
T1 - PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1267
ER -
. (2016). PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy . IEEE SigPort. http://sigport.org/1267
, 2016. PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy . Available at: http://sigport.org/1267.
. (2016). "PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy ." Web.
1. . PCA based Algorithm for Longitudinal Brain Tumor Stage Classification & Dynamical Modeling of Tumor Decay in response to VB-111 Virotherapy [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1267

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