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Medical image analysis

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: Nov. 13, 2018.
@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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poster

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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: Nov. 13, 2018.
@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: Nov. 13, 2018.
@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

ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES

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Authors:
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj
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6 October 2018 - 6:22pm
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ICIP2018_2065_slide_v2.pptx

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[1] Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj, "ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3579. Accessed: Nov. 13, 2018.
@article{3579-18,
url = {http://sigport.org/3579},
author = {Xiaoxi Pan; Mouloud Adel; Caroline Fossati; Thierry Gaidon; Eric Guedj },
publisher = {IEEE SigPort},
title = {ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES},
year = {2018} }
TY - EJOUR
T1 - ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES
AU - Xiaoxi Pan; Mouloud Adel; Caroline Fossati; Thierry Gaidon; Eric Guedj
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3579
ER -
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. (2018). ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES. IEEE SigPort. http://sigport.org/3579
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj, 2018. ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES. Available at: http://sigport.org/3579.
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. (2018). "ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES." Web.
1. Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3579

VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE


Numerous recent papers have demonstrated the utility of graph theoretical analysis in conjunction with sparse inverse covariance estimation (SICE) in understanding the modulation of brain connectivity associated with neuropathology. These concepts may complement established knowledge of functional covariance obtained using principal component analysis (PCA) that can reduce whole data representations of brain data to essential disease specific patterns.

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Authors:
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg
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7 October 2018 - 11:56pm
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SSM-PCA / SICE-GLASSO PD SUBNETWORK VISUALIZATION

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[1] Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg, "VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3571. Accessed: Nov. 13, 2018.
@article{3571-18,
url = {http://sigport.org/3571},
author = {Phoebe G. Spetsieris; Vijay Dhawan; David Eidelberg },
publisher = {IEEE SigPort},
title = {VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE},
year = {2018} }
TY - EJOUR
T1 - VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE
AU - Phoebe G. Spetsieris; Vijay Dhawan; David Eidelberg
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3571
ER -
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. (2018). VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE. IEEE SigPort. http://sigport.org/3571
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg, 2018. VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE. Available at: http://sigport.org/3571.
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. (2018). "VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE." Web.
1. Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3571

Deep Networks with Shape Priors for Nucleus Detection


Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing.

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Authors:
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga
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6 October 2018 - 1:43am
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[1] Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga, "Deep Networks with Shape Priors for Nucleus Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3561. Accessed: Nov. 13, 2018.
@article{3561-18,
url = {http://sigport.org/3561},
author = {Mohammad Tofighi; Tiantong Guo; Jairam K.P. Vanamala; Vishal Monga },
publisher = {IEEE SigPort},
title = {Deep Networks with Shape Priors for Nucleus Detection},
year = {2018} }
TY - EJOUR
T1 - Deep Networks with Shape Priors for Nucleus Detection
AU - Mohammad Tofighi; Tiantong Guo; Jairam K.P. Vanamala; Vishal Monga
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3561
ER -
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. (2018). Deep Networks with Shape Priors for Nucleus Detection. IEEE SigPort. http://sigport.org/3561
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga, 2018. Deep Networks with Shape Priors for Nucleus Detection. Available at: http://sigport.org/3561.
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. (2018). "Deep Networks with Shape Priors for Nucleus Detection." Web.
1. Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. Deep Networks with Shape Priors for Nucleus Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3561

DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS

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Authors:
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga
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5 October 2018 - 4:05pm
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ICIP_VENKAT_SLIDES

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[1] venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga, "DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3554. Accessed: Nov. 13, 2018.
@article{3554-18,
url = {http://sigport.org/3554},
author = {venkateswararao cherukuri; tiantong guo; steven j schiff; vishal monga },
publisher = {IEEE SigPort},
title = {DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS},
year = {2018} }
TY - EJOUR
T1 - DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS
AU - venkateswararao cherukuri; tiantong guo; steven j schiff; vishal monga
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3554
ER -
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. (2018). DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS. IEEE SigPort. http://sigport.org/3554
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga, 2018. DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS. Available at: http://sigport.org/3554.
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. (2018). "DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS." Web.
1. venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3554

MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS


We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T-cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immunotherapy cancer treatment.

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Authors:
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha
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5 October 2018 - 8:22am
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Poster ICIP

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[1] Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha, "MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3538. Accessed: Nov. 13, 2018.
@article{3538-18,
url = {http://sigport.org/3538},
author = {Fidel A. Guerrero Peña; Pedro D. Marrero Fernandez; Tsang Ing Ren; Mary Yui; Ellen Rothenberg; Alexandre Cunha },
publisher = {IEEE SigPort},
title = {MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS},
year = {2018} }
TY - EJOUR
T1 - MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS
AU - Fidel A. Guerrero Peña; Pedro D. Marrero Fernandez; Tsang Ing Ren; Mary Yui; Ellen Rothenberg; Alexandre Cunha
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3538
ER -
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. (2018). MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS. IEEE SigPort. http://sigport.org/3538
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha, 2018. MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS. Available at: http://sigport.org/3538.
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. (2018). "MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS." Web.
1. Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3538

CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING


We present a region based method for segmenting and splitting
images of cells in an automatic and unsupervised manner.
The detection of cell nuclei is based on the Bradley’s method.
False positives are automatically identified and rejected based
on shape and intensity features. Additionally, the proposed
method is able to automatically detect and split touching cells.
To do so, we employ a variant of a region based multi-ellipse
fitting method (DEFA) that makes use of constraints on the

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Authors:
Costas Panagiotakis, Antonis A. Argyros
Submitted On:
5 October 2018 - 3:50am
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CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING

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[1] Costas Panagiotakis, Antonis A. Argyros, "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3514. Accessed: Nov. 13, 2018.
@article{3514-18,
url = {http://sigport.org/3514},
author = {Costas Panagiotakis; Antonis A. Argyros },
publisher = {IEEE SigPort},
title = {CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING},
year = {2018} }
TY - EJOUR
T1 - CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING
AU - Costas Panagiotakis; Antonis A. Argyros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3514
ER -
Costas Panagiotakis, Antonis A. Argyros. (2018). CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. IEEE SigPort. http://sigport.org/3514
Costas Panagiotakis, Antonis A. Argyros, 2018. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. Available at: http://sigport.org/3514.
Costas Panagiotakis, Antonis A. Argyros. (2018). "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING." Web.
1. Costas Panagiotakis, Antonis A. Argyros. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3514

LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES


Retinal vessel information is helpful in retinal disease screening and diagnosis. Retinal vessel segmentation provides useful information about vessels and can be used by physicians during intraocular surgery and retinal diagnostic operations. Convolutional neural networks (CNNs) are powerful tools for classification and segmentation of medical images. However, complexity of CNNs makes it difficult to implement them in portable devices such as binocular indirect ophthalmoscopes. In this paper a simplification approach is proposed for CNNs based on combination of quantization and pruning.

Poster-Retina.pdf

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Authors:
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr
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4 October 2018 - 4:50pm
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[1] Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, "LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3465. Accessed: Nov. 13, 2018.
@article{3465-18,
url = {http://sigport.org/3465},
author = {Mohsen Hajabdollahi; Reza Esfandiarpoor; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr },
publisher = {IEEE SigPort},
title = {LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES},
year = {2018} }
TY - EJOUR
T1 - LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES
AU - Mohsen Hajabdollahi; Reza Esfandiarpoor; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3465
ER -
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES. IEEE SigPort. http://sigport.org/3465
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, 2018. LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES. Available at: http://sigport.org/3465.
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). "LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES." Web.
1. Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3465

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