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Medical image analysis

A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle


Left ventricle (LV) segmentation is crucial for quantitative analysis of the cardiac contractile function. In this paper, we propose a joint multi-scale convolutional neural network to fully automatically segment the LV. Our method adopts two kinds of multi-scale features of cardiac magnetic resonance (CMR) images, including multi-scale features directly extracted from CMR images with different scales and multi-scale features constructed by intermediate layers of standard CNN architecture.

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Authors:
Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning
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21 September 2017 - 2:05am
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ICIP2017-3473-poster.pdf

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[1] Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning, "A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2242. Accessed: Oct. 18, 2017.
@article{2242-17,
url = {http://sigport.org/2242},
author = {Qianqian Tong; Zhiyong Yuan; Xiangyun Liao; Mianlun Zheng; Weixu Zhu; Guian Zhang; Munan Ning },
publisher = {IEEE SigPort},
title = {A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle},
year = {2017} }
TY - EJOUR
T1 - A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle
AU - Qianqian Tong; Zhiyong Yuan; Xiangyun Liao; Mianlun Zheng; Weixu Zhu; Guian Zhang; Munan Ning
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2242
ER -
Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning. (2017). A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle. IEEE SigPort. http://sigport.org/2242
Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning, 2017. A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle. Available at: http://sigport.org/2242.
Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning. (2017). "A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle." Web.
1. Qianqian Tong, Zhiyong Yuan, Xiangyun Liao, Mianlun Zheng, Weixu Zhu, Guian Zhang, Munan Ning. A Joint Multi-scale Convolutional Network for Fully Automatic Segmentation of the Left Ventricle [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2242

WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES


Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are skeptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class.

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Authors:
Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch
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16 September 2017 - 10:03am
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ICIP_2017_Koehler_localization_diabetic_retinopathy_retina_3463.pdf

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[1] Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch, "WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2194. Accessed: Oct. 18, 2017.
@article{2194-17,
url = {http://sigport.org/2194},
author = {Waleed M. Gondal; Jan M. Koehler; Rène Grzeszick; Gernot A. Fink; and Michael Hirsch },
publisher = {IEEE SigPort},
title = {WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES},
year = {2017} }
TY - EJOUR
T1 - WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES
AU - Waleed M. Gondal; Jan M. Koehler; Rène Grzeszick; Gernot A. Fink; and Michael Hirsch
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2194
ER -
Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch. (2017). WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES. IEEE SigPort. http://sigport.org/2194
Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch, 2017. WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES. Available at: http://sigport.org/2194.
Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch. (2017). "WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES." Web.
1. Waleed M. Gondal, Jan M. Koehler, Rène Grzeszick, Gernot A. Fink, and Michael Hirsch. WEAKLY-SUPERVISED LOCALIZATION OF DIABETIC RETINOPATHY LESIONS IN RETINAL FUNDUS IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2194

CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION


Optic Disc (OD) detection in retinal fundus images is a cru-cial stage for the automation of a screening system in diabetic ophthalmology. Most researches for automatic localization of OD benefit the regions of vessels. In this paper, we present a fast and novel method based on the Circlet Transform to detect OD in digital retinal fundus images that doesn’t utilize the location of the vessels. First, each R, G and B band is enhanced using CLAHE method. Then, the enhanced image in RGB color space is converted to L*a*b one.

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Authors:
Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi
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15 September 2017 - 10:05am
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2017 ICIP Optic Disk Poster.pdf

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[1] Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi, "CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2137. Accessed: Oct. 18, 2017.
@article{2137-17,
url = {http://sigport.org/2137},
author = {Omid Sarrafzadeh; Hossein Rabbani; Alireza Mehri Dehnavi },
publisher = {IEEE SigPort},
title = {CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION},
year = {2017} }
TY - EJOUR
T1 - CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION
AU - Omid Sarrafzadeh; Hossein Rabbani; Alireza Mehri Dehnavi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2137
ER -
Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi. (2017). CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION. IEEE SigPort. http://sigport.org/2137
Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi, 2017. CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION. Available at: http://sigport.org/2137.
Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi. (2017). "CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION." Web.
1. Omid Sarrafzadeh, Hossein Rabbani, Alireza Mehri Dehnavi. CIRCLET BASED FRAMEWORK FOR OPTIC DISK DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2137

DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES


Accurate detection of microaneurysm (MA) plays a very important role in early diagnosis of diabetic retinopathy. This paper presents a novel method based on the variation of local intensity for microaneurysms detection in retinal images. In contribution, proposed method use local rank transform effectively

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Authors:
Ravi Kamble , Manesh Kokare
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15 September 2017 - 5:02am
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[1] Ravi Kamble , Manesh Kokare, "DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2113. Accessed: Oct. 18, 2017.
@article{2113-17,
url = {http://sigport.org/2113},
author = {Ravi Kamble ; Manesh Kokare },
publisher = {IEEE SigPort},
title = {DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES},
year = {2017} }
TY - EJOUR
T1 - DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES
AU - Ravi Kamble ; Manesh Kokare
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2113
ER -
Ravi Kamble , Manesh Kokare. (2017). DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES. IEEE SigPort. http://sigport.org/2113
Ravi Kamble , Manesh Kokare, 2017. DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES. Available at: http://sigport.org/2113.
Ravi Kamble , Manesh Kokare. (2017). "DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES." Web.
1. Ravi Kamble , Manesh Kokare. DETECTION OF MICROANEURYSM USING LOCAL RANK TRANSFORM IN COLOR FUNDUS IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2113

Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation


Automated segmentation of retinal blood vessels plays an important role in the computer aided diagnosis of retinal diseases. The paper presents a new formulation of patch-based fully Convolutional Neural Networks (CNNs) that allows accurate segmentation of the retinal blood vessels. A major modification in this retinal blood vessel segmentation task is to improve and speed-up the patch-based fully CNN training by local entropy sampling and a skip CNN architecture with class-balancing loss.

icip2017.pdf

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Authors:
Jie Yang, Lixiu Yao
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14 September 2017 - 4:07am
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[1] Jie Yang, Lixiu Yao, "Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1998. Accessed: Oct. 18, 2017.
@article{1998-17,
url = {http://sigport.org/1998},
author = {Jie Yang; Lixiu Yao },
publisher = {IEEE SigPort},
title = {Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation},
year = {2017} }
TY - EJOUR
T1 - Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation
AU - Jie Yang; Lixiu Yao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1998
ER -
Jie Yang, Lixiu Yao. (2017). Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation. IEEE SigPort. http://sigport.org/1998
Jie Yang, Lixiu Yao, 2017. Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation. Available at: http://sigport.org/1998.
Jie Yang, Lixiu Yao. (2017). "Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation." Web.
1. Jie Yang, Lixiu Yao. Patch-based Fully Convolutional Neural Network With Skip Connections For Retinal Blood Vessel Segmentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1998

Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features


Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs
of cancer in mammograms. In these CAD systems, mass segmentation plays a central role in the decision process. In the
literature, mass segmentation has been typically evaluated in a intra-sensor scenario, where the methodology is designed and
evaluated in similar data. However, in practice, acquisition systems and PACS from multiple vendors abound and current

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Authors:
Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley
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11 September 2017 - 12:50pm
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[1] Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley, "Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1914. Accessed: Oct. 18, 2017.
@article{1914-17,
url = {http://sigport.org/1914},
author = {Jaime S. Cardoso; Nuno Marques; Neeraj Dhungel; Gustavo Carneiro; Andrew Bradley },
publisher = {IEEE SigPort},
title = {Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features},
year = {2017} }
TY - EJOUR
T1 - Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features
AU - Jaime S. Cardoso; Nuno Marques; Neeraj Dhungel; Gustavo Carneiro; Andrew Bradley
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1914
ER -
Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley. (2017). Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features. IEEE SigPort. http://sigport.org/1914
Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley, 2017. Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features. Available at: http://sigport.org/1914.
Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley. (2017). "Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features." Web.
1. Jaime S. Cardoso, Nuno Marques, Neeraj Dhungel, Gustavo Carneiro, Andrew Bradley. Mass Segmentation in Mammograms: a Cross-Sensor comparison of deep and tailored features [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1914

Microvasculature Segmentation of Arterioles Using Deep CNN


Segmenting microvascular structures is an important requirement in understanding angioadaptation by which vascular networks remodel their morphological structures. Accurate segmentation for separating microvasculature structures is important in quantifying remodeling process. In this work, we utilize a deep convolutional neural network (CNN) framework for obtaining robust segmentations of microvasculature from epifluorescence microscopy imagery of mice dura mater.

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Authors:
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan
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10 September 2017 - 10:05pm
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[1] Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan, "Microvasculature Segmentation of Arterioles Using Deep CNN", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1891. Accessed: Oct. 18, 2017.
@article{1891-17,
url = {http://sigport.org/1891},
author = {Yasmin M. Kassim; V. B. Surya Prasath; Olga V. Glinskii; Vladislav V. Glinsky; Virginia H. Huxley; Kannappan Palaniappan },
publisher = {IEEE SigPort},
title = {Microvasculature Segmentation of Arterioles Using Deep CNN},
year = {2017} }
TY - EJOUR
T1 - Microvasculature Segmentation of Arterioles Using Deep CNN
AU - Yasmin M. Kassim; V. B. Surya Prasath; Olga V. Glinskii; Vladislav V. Glinsky; Virginia H. Huxley; Kannappan Palaniappan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1891
ER -
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. (2017). Microvasculature Segmentation of Arterioles Using Deep CNN. IEEE SigPort. http://sigport.org/1891
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan, 2017. Microvasculature Segmentation of Arterioles Using Deep CNN. Available at: http://sigport.org/1891.
Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. (2017). "Microvasculature Segmentation of Arterioles Using Deep CNN." Web.
1. Yasmin M. Kassim, V. B. Surya Prasath, Olga V. Glinskii, Vladislav V. Glinsky, Virginia H. Huxley, Kannappan Palaniappan. Microvasculature Segmentation of Arterioles Using Deep CNN [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1891

ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES


In this paper, we presented an automatic method for BVS struts detection in IVOCT images based on Haar-like features and Adaboost algorithm. Then, DP algorithm was used for struts segmentation. Based on the detection and segmentation results, apposed and malapposed struts were distinguished automatically. The qualitative and quantitative evaluation shows that our method is effective and robust for BVS struts detection and segmentation, and is capable of malapposition analysis.

ICIP_POSTER2501.pdf

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Authors:
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao
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4 September 2017 - 11:30pm
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[1] Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao, "ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1829. Accessed: Oct. 18, 2017.
@article{1829-17,
url = {http://sigport.org/1829},
author = {Yifeng Lu; Yihui Cao; Qinhua Jin; Yundai Chen; Qinye Yin; Jianan Li; Rui Zhu; Wei Zhao },
publisher = {IEEE SigPort},
title = {ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES},
year = {2017} }
TY - EJOUR
T1 - ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES
AU - Yifeng Lu; Yihui Cao; Qinhua Jin; Yundai Chen; Qinye Yin; Jianan Li; Rui Zhu; Wei Zhao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1829
ER -
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. (2017). ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES. IEEE SigPort. http://sigport.org/1829
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao, 2017. ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES. Available at: http://sigport.org/1829.
Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. (2017). "ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES." Web.
1. Yifeng Lu, Yihui Cao, Qinhua Jin, Yundai Chen, Qinye Yin, Jianan Li, Rui Zhu, Wei Zhao. ADABOOST-BASED DETECTION AND SEGMENTATION OF BIORESORBABLE VASCULAR SCAFFOLDS STRUTS IN IVOCT IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1829

Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia


Numerous studies have shown that brain functional connectivity patterns can be time-varying over periods of tens of seconds. K-means has been widely used to extract the connectivity states from dynamic functional connectivity. However, K-means is dependent on initialization and can be exponentially slow in converging due to extensive noise in dynamic functional connectivity. In this work, we propose to use an affinity propagation clustering method to estimate the connectivity states. The new approach found more meaningful group differences than K-means. Our finding supports that our method is promising in exploring biomarkers of mental disorders.

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Salman M. S., Du Y., Calhoun V. D.
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10 March 2017 - 9:19pm
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[1] Salman M. S., Du Y., Calhoun V. D., "Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1740. Accessed: Oct. 18, 2017.
@article{1740-17,
url = {http://sigport.org/1740},
author = {Salman M. S.; Du Y.; Calhoun V. D. },
publisher = {IEEE SigPort},
title = {Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia},
year = {2017} }
TY - EJOUR
T1 - Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia
AU - Salman M. S.; Du Y.; Calhoun V. D.
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1740
ER -
Salman M. S., Du Y., Calhoun V. D.. (2017). Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia. IEEE SigPort. http://sigport.org/1740
Salman M. S., Du Y., Calhoun V. D., 2017. Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia. Available at: http://sigport.org/1740.
Salman M. S., Du Y., Calhoun V. D.. (2017). "Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia." Web.
1. Salman M. S., Du Y., Calhoun V. D.. Identifying fMRI Dynamic Connectivity States Using Affinity Propagation Clustering Method: Application to Schizophrenia [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1740

Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features


Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification.

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Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun
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8 March 2017 - 3:35am
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Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features

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[1] Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1701. Accessed: Oct. 18, 2017.
@article{1701-17,
url = {http://sigport.org/1701},
author = {Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun },
publisher = {IEEE SigPort},
title = {Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features},
year = {2017} }
TY - EJOUR
T1 - Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features
AU - Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1701
ER -
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. IEEE SigPort. http://sigport.org/1701
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, 2017. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. Available at: http://sigport.org/1701.
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features." Web.
1. Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1701

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