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Medical image analysis

An enhanced deep learning architecture for classification of tuberculosis types from CT lung images

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Authors:
Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan
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3 November 2020 - 9:39am
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[1] Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan , "An enhanced deep learning architecture for classification of tuberculosis types from CT lung images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5521. Accessed: Nov. 26, 2020.
@article{5521-20,
url = {http://sigport.org/5521},
author = {Xiaohong Gao; Richard Compley; Maleika Heenaye-Mamode Khan },
publisher = {IEEE SigPort},
title = {An enhanced deep learning architecture for classification of tuberculosis types from CT lung images},
year = {2020} }
TY - EJOUR
T1 - An enhanced deep learning architecture for classification of tuberculosis types from CT lung images
AU - Xiaohong Gao; Richard Compley; Maleika Heenaye-Mamode Khan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5521
ER -
Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan . (2020). An enhanced deep learning architecture for classification of tuberculosis types from CT lung images. IEEE SigPort. http://sigport.org/5521
Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan , 2020. An enhanced deep learning architecture for classification of tuberculosis types from CT lung images. Available at: http://sigport.org/5521.
Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan . (2020). "An enhanced deep learning architecture for classification of tuberculosis types from CT lung images." Web.
1. Xiaohong Gao, Richard Compley, Maleika Heenaye-Mamode Khan . An enhanced deep learning architecture for classification of tuberculosis types from CT lung images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5521

An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images

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Authors:
Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang
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3 November 2020 - 12:36am
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An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images.pdf

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[1] Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang, "An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5503. Accessed: Nov. 26, 2020.
@article{5503-20,
url = {http://sigport.org/5503},
author = {Lifeng Yan; Xiaoqing Liu; Yizhou Yu; Sanyuan Zhang },
publisher = {IEEE SigPort},
title = {An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images},
year = {2020} }
TY - EJOUR
T1 - An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images
AU - Lifeng Yan; Xiaoqing Liu; Yizhou Yu; Sanyuan Zhang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5503
ER -
Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang. (2020). An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images. IEEE SigPort. http://sigport.org/5503
Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang, 2020. An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images. Available at: http://sigport.org/5503.
Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang. (2020). "An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images." Web.
1. Lifeng Yan, Xiaoqing Liu, Yizhou Yu, Sanyuan Zhang. An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray Images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5503

DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK

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Authors:
Mohammad Madine, Islem Rekik, Naoufel Werghi
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2 November 2020 - 5:32pm
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DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING.pdf

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[1] Mohammad Madine, Islem Rekik, Naoufel Werghi, "DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5493. Accessed: Nov. 26, 2020.
@article{5493-20,
url = {http://sigport.org/5493},
author = {Mohammad Madine; Islem Rekik; Naoufel Werghi },
publisher = {IEEE SigPort},
title = {DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK},
year = {2020} }
TY - EJOUR
T1 - DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK
AU - Mohammad Madine; Islem Rekik; Naoufel Werghi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5493
ER -
Mohammad Madine, Islem Rekik, Naoufel Werghi. (2020). DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK. IEEE SigPort. http://sigport.org/5493
Mohammad Madine, Islem Rekik, Naoufel Werghi, 2020. DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK. Available at: http://sigport.org/5493.
Mohammad Madine, Islem Rekik, Naoufel Werghi. (2020). "DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK." Web.
1. Mohammad Madine, Islem Rekik, Naoufel Werghi. DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5493

A segmentation based deep learning framework for multimodal retinal image registration


Multimodal image registration plays an important role in diagnosing and treating ophthalmologic diseases. In this paper, a deep learning framework for multimodal retinal image registration is proposed. The framework consists of a segmentation network, feature detection and description network, and an outlier rejection network, which focuses only on the globally coarse alignment step using the perspective transformation.

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Authors:
Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen
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13 May 2020 - 4:42pm
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[1] Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen, "A segmentation based deep learning framework for multimodal retinal image registration", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5134. Accessed: Nov. 26, 2020.
@article{5134-20,
url = {http://sigport.org/5134},
author = {Yiqian Wang; Junkang Zhang; Cheolhong An; Melina Cavichini; Mahima Jhingan; Manuel J. Amador-Patarroyo; Christopher P. Long; Dirk-Uwe G. Bartsch; William R. Freeman; Truong Q. Nguyen },
publisher = {IEEE SigPort},
title = {A segmentation based deep learning framework for multimodal retinal image registration},
year = {2020} }
TY - EJOUR
T1 - A segmentation based deep learning framework for multimodal retinal image registration
AU - Yiqian Wang; Junkang Zhang; Cheolhong An; Melina Cavichini; Mahima Jhingan; Manuel J. Amador-Patarroyo; Christopher P. Long; Dirk-Uwe G. Bartsch; William R. Freeman; Truong Q. Nguyen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5134
ER -
Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen. (2020). A segmentation based deep learning framework for multimodal retinal image registration. IEEE SigPort. http://sigport.org/5134
Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen, 2020. A segmentation based deep learning framework for multimodal retinal image registration. Available at: http://sigport.org/5134.
Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen. (2020). "A segmentation based deep learning framework for multimodal retinal image registration." Web.
1. Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen. A segmentation based deep learning framework for multimodal retinal image registration [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5134

A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION


Biomedical image segmentation has been widely studied, and lots of methods have been proposed. Among these methods, attention U-Net has achieved a promising performance. However, it has drawbacks of extracting the multi-scaled receptive field features at the high-level feature maps, resulting in the degeneration when dealing with the lesions with apparent scale variations. To solve this problem, this paper integrates an atrous spatial pyramid pooling (ASPP) module in the contracting path of attention U-Net.

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Authors:
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong
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12 February 2020 - 12:23pm
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Biomedical image segmentation

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[1] Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong, "A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4983. Accessed: Nov. 26, 2020.
@article{4983-20,
url = {http://sigport.org/4983},
author = {Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong },
publisher = {IEEE SigPort},
title = {A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION},
year = {2020} }
TY - EJOUR
T1 - A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION
AU - Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4983
ER -
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. (2020). A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/4983
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong, 2020. A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION. Available at: http://sigport.org/4983.
Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. (2020). "A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION." Web.
1. Pengcheng Guo Xiangdong Su Haoran Zhang Meng Wang Feilong. A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4983

Lightweight V-Net for Liver Segmentation


The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To address these issues, we design a lightweight V-Net (LV-Net) for liver segmentation in this paper. The proposed network makes two contributions. The first is that we design an inverted residual bottleneck block (IRB block) and a 3D average pooling block and apply them to the proposed LV-Net.

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Authors:
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi
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6 February 2020 - 5:47am
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[1] Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi, "Lightweight V-Net for Liver Segmentation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4972. Accessed: Nov. 26, 2020.
@article{4972-20,
url = {http://sigport.org/4972},
author = {Wenzheng Zhou; Yuxiao Zhang;Risheng Wang; Hongying Meng; asoke K. Nandi },
publisher = {IEEE SigPort},
title = {Lightweight V-Net for Liver Segmentation},
year = {2020} }
TY - EJOUR
T1 - Lightweight V-Net for Liver Segmentation
AU - Wenzheng Zhou; Yuxiao Zhang;Risheng Wang; Hongying Meng; asoke K. Nandi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4972
ER -
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. (2020). Lightweight V-Net for Liver Segmentation. IEEE SigPort. http://sigport.org/4972
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi, 2020. Lightweight V-Net for Liver Segmentation. Available at: http://sigport.org/4972.
Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. (2020). "Lightweight V-Net for Liver Segmentation." Web.
1. Wenzheng Zhou, Yuxiao Zhang,Risheng Wang, Hongying Meng, asoke K. Nandi. Lightweight V-Net for Liver Segmentation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4972

Deep Learning Based Mass Detection in Mammograms


Mammogram is the primary imaging technique for breast cancer screening, the leading type of cancer in women worldwide. While the clinical effectiveness of mammogram has been well demonstrated, the mammographic characteristics of breast masses are quite complex. As a result, radiologists certified for reading mammography are lacking, which limits the accessibility of mammography for more population. In this paper, we propose a Computer Aided Detection (CADe) method to automatically detect masses in mammography.

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Authors:
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma
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7 November 2019 - 8:29pm
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[1] Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma, "Deep Learning Based Mass Detection in Mammograms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4928. Accessed: Nov. 26, 2020.
@article{4928-19,
url = {http://sigport.org/4928},
author = {Zhenjie Cao; Zhicheng Yang; Yanbo Zhang; Ruei-Sung Lin; Shibin Wu; Lingyun Huang; Mei Han; Jie Ma },
publisher = {IEEE SigPort},
title = {Deep Learning Based Mass Detection in Mammograms},
year = {2019} }
TY - EJOUR
T1 - Deep Learning Based Mass Detection in Mammograms
AU - Zhenjie Cao; Zhicheng Yang; Yanbo Zhang; Ruei-Sung Lin; Shibin Wu; Lingyun Huang; Mei Han; Jie Ma
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4928
ER -
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. (2019). Deep Learning Based Mass Detection in Mammograms. IEEE SigPort. http://sigport.org/4928
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma, 2019. Deep Learning Based Mass Detection in Mammograms. Available at: http://sigport.org/4928.
Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. (2019). "Deep Learning Based Mass Detection in Mammograms." Web.
1. Zhenjie Cao, Zhicheng Yang, Yanbo Zhang, Ruei-Sung Lin, Shibin Wu, Lingyun Huang, Mei Han, Jie Ma. Deep Learning Based Mass Detection in Mammograms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4928

A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI


The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the k-space (Fourier domain). Deep learning techniques have been recently received considerable interest for accelerating MR imaging (MRI). In this paper, a deep learning method for accelerating MRI is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding.

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Authors:
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini
Submitted On:
7 November 2019 - 5:38am
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[1] Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini, "A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4919. Accessed: Nov. 26, 2020.
@article{4919-19,
url = {http://sigport.org/4919},
author = {Antonio Falvo; Danilo Comminiello; Simone Scardapane; Michele Scarpiniti; Aurelio Uncini },
publisher = {IEEE SigPort},
title = {A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI},
year = {2019} }
TY - EJOUR
T1 - A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI
AU - Antonio Falvo; Danilo Comminiello; Simone Scardapane; Michele Scarpiniti; Aurelio Uncini
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4919
ER -
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. (2019). A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI. IEEE SigPort. http://sigport.org/4919
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini, 2019. A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI. Available at: http://sigport.org/4919.
Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. (2019). "A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI." Web.
1. Antonio Falvo, Danilo Comminiello, Simone Scardapane, Michele Scarpiniti, Aurelio Uncini. A Multimodal Dense U-Net for Accelerating Multiple Sclerosis MRI [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4919

Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data


Quality scores assigned to blastocyst inner cell mass (ICM), trophectoderm (TE), and zona pellucida (ZP) are critical markers for predicting implantation potential of a human blastocyst in IVF treatment. Deep Convolutional Neural Networks (CNNs) have shown success in various image classification tasks, including classification of blastocysts into two quality categories. However, the problem of multi-label multi-class classification for blastocyst grading remains unsolved.

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Authors:
Parvaneh Saeedi, Jason Au, Jon Havelock
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24 September 2019 - 6:18pm
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[1] Parvaneh Saeedi, Jason Au, Jon Havelock, "Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4837. Accessed: Nov. 26, 2020.
@article{4837-19,
url = {http://sigport.org/4837},
author = {Parvaneh Saeedi; Jason Au; Jon Havelock },
publisher = {IEEE SigPort},
title = {Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data},
year = {2019} }
TY - EJOUR
T1 - Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data
AU - Parvaneh Saeedi; Jason Au; Jon Havelock
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4837
ER -
Parvaneh Saeedi, Jason Au, Jon Havelock. (2019). Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data. IEEE SigPort. http://sigport.org/4837
Parvaneh Saeedi, Jason Au, Jon Havelock, 2019. Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data. Available at: http://sigport.org/4837.
Parvaneh Saeedi, Jason Au, Jon Havelock. (2019). "Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data." Web.
1. Parvaneh Saeedi, Jason Au, Jon Havelock. Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4837

PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS


A quantitative understanding of complex biological systems such as tissues requires reconstructing the structure of the different components of the system. Fluorescence microscopy provides the means to visualize simultaneously several tissue components. However, it can be time consuming and is limited by the number of fluorescent markers that can be used. In this study, we describe a toolbox of algorithms based on convolutional neural networks for the prediction of 3D tissue structures by learning features embedded within single-marker images.

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Authors:
Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis
Submitted On:
20 September 2019 - 7:25am
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PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

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[1] Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis, "PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4776. Accessed: Nov. 26, 2020.
@article{4776-19,
url = {http://sigport.org/4776},
author = {Hernan Morales-Navarrete; Fabian Segovia-Miranda; Marino Zerial and Yannis Kalaidzidis },
publisher = {IEEE SigPort},
title = {PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
AU - Hernan Morales-Navarrete; Fabian Segovia-Miranda; Marino Zerial and Yannis Kalaidzidis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4776
ER -
Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis. (2019). PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4776
Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis, 2019. PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4776.
Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis. (2019). "PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Hernan Morales-Navarrete, Fabian Segovia-Miranda, Marino Zerial and Yannis Kalaidzidis. PREDICTION OF MULTIPLE 3D TISSUE STRUCTURES BASED ON SINGLE-MARKER IMAGES USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4776

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