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Biomedical signal processing

Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion


Pathological Hand Tremor (PHT) is one of the most prevalent symptoms of some neurological movement disorders such as Parkinson’s Disease (PD) and Essential Tremor (ET). Characterization, estimation, and extraction of PHT is a crucial requirement for assistive and robotic rehabilitation technologies that aim to counteract or resist PHT as an input noise to the system. In general, research in the literature on the topic of PHT removal can be categorized into two major categories, namely, classic and data-driven methods.

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Authors:
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi
Submitted On:
8 November 2019 - 7:28pm
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Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion

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[1] Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi, "Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4935. Accessed: Dec. 13, 2019.
@article{4935-19,
url = {http://sigport.org/4935},
author = {Soroosh Shahtalebi; S. Farokh Atashzar; Rajni V. Patel; Arash Mohammadi },
publisher = {IEEE SigPort},
title = {Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion},
year = {2019} }
TY - EJOUR
T1 - Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion
AU - Soroosh Shahtalebi; S. Farokh Atashzar; Rajni V. Patel; Arash Mohammadi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4935
ER -
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. (2019). Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion. IEEE SigPort. http://sigport.org/4935
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi, 2019. Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion. Available at: http://sigport.org/4935.
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. (2019). "Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion." Web.
1. Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4935

AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications


For their analysis with conventional signal processing tools, non-stationary signals are assumed to be stationary (or at least wide-sense stationary) in short intervals. While this approach allows them to be studied, it disregards the temporal evolution of their statistics. As such, to analyze this type of signals, it is desirable to use a representation that registers and characterizes the temporal changes in the frequency content of the signals, as these changes may occur in single or multiple periodic ways.

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Authors:
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk
Submitted On:
7 November 2019 - 7:21pm
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Poster

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[1] Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4927. Accessed: Dec. 13, 2019.
@article{4927-19,
url = {http://sigport.org/4927},
author = {Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk },
publisher = {IEEE SigPort},
title = {AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications},
year = {2019} }
TY - EJOUR
T1 - AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications
AU - Raymundo Cassani; Isabela Albuquerque; Joao Monteiro; Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4927
ER -
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. IEEE SigPort. http://sigport.org/4927
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk, 2019. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications. Available at: http://sigport.org/4927.
Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. (2019). "AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications." Web.
1. Raymundo Cassani, Isabela Albuquerque, Joao Monteiro, Tiago H. Falk. AMA: An Open-source Amplitude Modulation Analysis Toolkit for Signal Processing Applications [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4927

Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach

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Authors:
C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel
Submitted On:
13 November 2019 - 11:22am
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Rician_presentation.pdf

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[1] C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel, "Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4914. Accessed: Dec. 13, 2019.
@article{4914-19,
url = {http://sigport.org/4914},
author = {C. Chatzichristos; M. Vandecapelle; E. Kofidis; S. Theodoridis; L. De Lathauwer and S. Van Huffel },
publisher = {IEEE SigPort},
title = {Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach},
year = {2019} }
TY - EJOUR
T1 - Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach
AU - C. Chatzichristos; M. Vandecapelle; E. Kofidis; S. Theodoridis; L. De Lathauwer and S. Van Huffel
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4914
ER -
C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel. (2019). Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach. IEEE SigPort. http://sigport.org/4914
C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel, 2019. Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach. Available at: http://sigport.org/4914.
C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel. (2019). "Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach." Web.
1. C. Chatzichristos, M. Vandecapelle, E. Kofidis, S. Theodoridis, L. De Lathauwer and S. Van Huffel. Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption – A β-Divergence Approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4914

A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators


Thermal analysis using resonating micro-electromechanical systems shows great promise in characterizing materials in the early stages of research. Through thermal cycles and actuation using a piezoelectric speaker, the resonant behaviour of a model drug, theophylline monohydrate, is measured across the surface whilst using a laser-Doppler vibrometer for readout. Acquired is a sequence of spectra that are strongly correlated in time, temperature and spatial location of the readout. Traditionally, each spectrum is analyzed individually to locate the resonance peak.

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Authors:
Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm
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24 October 2019 - 4:37am
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MLSP_2019_Poster_v7_final.pdf

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[1] Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm, "A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4884. Accessed: Dec. 13, 2019.
@article{4884-19,
url = {http://sigport.org/4884},
author = {Maximillian F. Vording; Peter O. Okeyo; Juan J. R. Guillamón; Peter E. Larsen; Mikkel N. Schmidt; Tommy S. Alstrøm },
publisher = {IEEE SigPort},
title = {A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators},
year = {2019} }
TY - EJOUR
T1 - A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators
AU - Maximillian F. Vording; Peter O. Okeyo; Juan J. R. Guillamón; Peter E. Larsen; Mikkel N. Schmidt; Tommy S. Alstrøm
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4884
ER -
Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm. (2019). A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. IEEE SigPort. http://sigport.org/4884
Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm, 2019. A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. Available at: http://sigport.org/4884.
Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm. (2019). "A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators." Web.
1. Maximillian F. Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm. A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4884

Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise


The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multiexponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced.

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Authors:
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.
Submitted On:
20 September 2019 - 11:12am
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ICIP_1976.pdf

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[1] EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja., "Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4783. Accessed: Dec. 13, 2019.
@article{4783-19,
url = {http://sigport.org/4783},
author = {EL HAJJ Christian; MOUSSAOUI Saïd; COLLEWET Guylaine; MUSSE Maja. },
publisher = {IEEE SigPort},
title = {Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise},
year = {2019} }
TY - EJOUR
T1 - Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise
AU - EL HAJJ Christian; MOUSSAOUI Saïd; COLLEWET Guylaine; MUSSE Maja.
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4783
ER -
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. (2019). Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise. IEEE SigPort. http://sigport.org/4783
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja., 2019. Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise. Available at: http://sigport.org/4783.
EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. (2019). "Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise." Web.
1. EL HAJJ Christian, MOUSSAOUI Saïd, COLLEWET Guylaine, MUSSE Maja.. Spatially Regularized Multi-exponential Transverse Relaxation Times Estimation from Magnitude Magnetic Resonance Images Under Rician Noise [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4783

DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT


Depression is a common mental disorder, which greatly affects the patients' daily life and work. Current depression detection relies almost exclusively on the clinical interview and structured questionnaire, consuming a lot of medical resources and risking a range of subjective biases. Our goal is to achieve a convenient and objective depression detection system, which can assist clinicians in their diagnosis of clinical depression.

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Authors:
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang
Submitted On:
11 September 2019 - 2:29am
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Eposter_ZeyuPan.pdf

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[1] Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang, "DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4589. Accessed: Dec. 13, 2019.
@article{4589-19,
url = {http://sigport.org/4589},
author = {Zeyu Pan; Huimin Ma; Lin Zhang; Yahui Wang },
publisher = {IEEE SigPort},
title = {DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT},
year = {2019} }
TY - EJOUR
T1 - DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT
AU - Zeyu Pan; Huimin Ma; Lin Zhang; Yahui Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4589
ER -
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. (2019). DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT. IEEE SigPort. http://sigport.org/4589
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang, 2019. DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT. Available at: http://sigport.org/4589.
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. (2019). "DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT." Web.
1. Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang. DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4589

ECG Reconstruction via PPG: A Pilot Study


In this paper, the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) signals is studied, and the waveform of ECG is inferred via the PPG signals. In order to address this inverse problem, a transform is proposed to map the discrete cosine transform (DCT) coefficients of each PPG cycle to those of the corresponding ECG cycle. The resulting DCT coefficients of the ECG cycle are inversely transformed to obtain the reconstructed ECG waveform. The proposed method is evaluated on a benchmark dataset of subjects with a variety of combinations of age and weight.

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Authors:
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu
Submitted On:
22 May 2019 - 5:16pm
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Presentation slides (pdf version)

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[1] Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu, "ECG Reconstruction via PPG: A Pilot Study", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4558. Accessed: Dec. 13, 2019.
@article{4558-19,
url = {http://sigport.org/4558},
author = {Qiang Zhu; Xin Tian; Chau-Wai Wong; Min Wu },
publisher = {IEEE SigPort},
title = {ECG Reconstruction via PPG: A Pilot Study},
year = {2019} }
TY - EJOUR
T1 - ECG Reconstruction via PPG: A Pilot Study
AU - Qiang Zhu; Xin Tian; Chau-Wai Wong; Min Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4558
ER -
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. (2019). ECG Reconstruction via PPG: A Pilot Study. IEEE SigPort. http://sigport.org/4558
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu, 2019. ECG Reconstruction via PPG: A Pilot Study. Available at: http://sigport.org/4558.
Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. (2019). "ECG Reconstruction via PPG: A Pilot Study." Web.
1. Qiang Zhu, Xin Tian, Chau-Wai Wong, Min Wu. ECG Reconstruction via PPG: A Pilot Study [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4558

BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG

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17 May 2019 - 6:04am
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presen_SIP_ICASSP.pdf

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[1] , "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4546. Accessed: Dec. 13, 2019.
@article{4546-19,
url = {http://sigport.org/4546},
author = { },
publisher = {IEEE SigPort},
title = {BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG},
year = {2019} }
TY - EJOUR
T1 - BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4546
ER -
. (2019). BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. IEEE SigPort. http://sigport.org/4546
, 2019. BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. Available at: http://sigport.org/4546.
. (2019). "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG." Web.
1. . BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4546

BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG

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BEAMFORMER_DESIGN_TIME-CORRELATED_INTERFERENCE

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[1] , "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4545. Accessed: Dec. 13, 2019.
@article{4545-19,
url = {http://sigport.org/4545},
author = { },
publisher = {IEEE SigPort},
title = {BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG},
year = {2019} }
TY - EJOUR
T1 - BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4545
ER -
. (2019). BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. IEEE SigPort. http://sigport.org/4545
, 2019. BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG. Available at: http://sigport.org/4545.
. (2019). "BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG." Web.
1. . BEAMFORMER DESIGN UNDER TIME-CORRELATED INTERFERENCE AND ONLINE IMPLEMENTATION: BRAIN-ACTIVITY RECONSTRUCTION FROM EEG [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4545

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: Dec. 13, 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

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