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Medical image analysis

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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[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: Sep. 30, 2020.
@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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[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: Sep. 30, 2020.
@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: Sep. 30, 2020.
@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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[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: Sep. 30, 2020.
@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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[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: Sep. 30, 2020.
@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
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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: Sep. 30, 2020.
@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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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: Sep. 30, 2020.
@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: Sep. 30, 2020.
@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

WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING


Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis, using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches.

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Authors:
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai
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9 October 2018 - 8:39am
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[1] Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3654. Accessed: Sep. 30, 2020.
@article{3654-18,
url = {http://sigport.org/3654},
author = {Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai },
publisher = {IEEE SigPort},
title = {WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING},
year = {2018} }
TY - EJOUR
T1 - WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING
AU - Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3654
ER -
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. IEEE SigPort. http://sigport.org/3654
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, 2018. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. Available at: http://sigport.org/3654.
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING." Web.
1. Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3654

3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images

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Authors:
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola
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8 October 2018 - 3:04am
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[1] Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola , "3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3613. Accessed: Sep. 30, 2020.
@article{3613-18,
url = {http://sigport.org/3613},
author = {Chenjie Ge; Qixun Qu; Irene YH Gu; Asgeir S Jakola },
publisher = {IEEE SigPort},
title = {3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images},
year = {2018} }
TY - EJOUR
T1 - 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images
AU - Chenjie Ge; Qixun Qu; Irene YH Gu; Asgeir S Jakola
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3613
ER -
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . (2018). 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images. IEEE SigPort. http://sigport.org/3613
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola , 2018. 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images. Available at: http://sigport.org/3613.
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . (2018). "3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images." Web.
1. Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3613

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