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Medical image analysis

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: Dec. 12, 2019.
@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
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7 November 2019 - 5:38am
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Poster

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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: Dec. 12, 2019.
@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
Submitted On:
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: Dec. 12, 2019.
@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
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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: Dec. 12, 2019.
@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

Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images

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18 September 2019 - 11:46pm
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[1] , "Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4703. Accessed: Dec. 12, 2019.
@article{4703-19,
url = {http://sigport.org/4703},
author = { },
publisher = {IEEE SigPort},
title = {Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images},
year = {2019} }
TY - EJOUR
T1 - Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4703
ER -
. (2019). Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images. IEEE SigPort. http://sigport.org/4703
, 2019. Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images. Available at: http://sigport.org/4703.
. (2019). "Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images." Web.
1. . Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic CT Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4703

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading


Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects.

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Authors:
Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu
Submitted On:
18 September 2019 - 11:15pm
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[1] Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu, "BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4700. Accessed: Dec. 12, 2019.
@article{4700-19,
url = {http://sigport.org/4700},
author = {Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu },
publisher = {IEEE SigPort},
title = {BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading},
year = {2019} }
TY - EJOUR
T1 - BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
AU - Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4700
ER -
Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu. (2019). BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading. IEEE SigPort. http://sigport.org/4700
Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu, 2019. BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading. Available at: http://sigport.org/4700.
Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu. (2019). "BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading." Web.
1. Ziyuan Zhao ; Kerui Zhang ; Xuejie Hao ; Jing Tian ; Matthew Chin Heng Chua ; Li Chen ; Xin Xu. BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4700

DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION


Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF_OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues.

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Authors:
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan
Submitted On:
18 September 2019 - 10:11pm
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[1] Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan, "DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4696. Accessed: Dec. 12, 2019.
@article{4696-19,
url = {http://sigport.org/4696},
author = {Yasmin M. Kassim; O. V. Glinskii; V. V. Glinsky; V. H. Huxley; G. Guidoboni; K. Palaniappan },
publisher = {IEEE SigPort},
title = {DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION
AU - Yasmin M. Kassim; O. V. Glinskii; V. V. Glinsky; V. H. Huxley; G. Guidoboni; K. Palaniappan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4696
ER -
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. (2019). DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION. IEEE SigPort. http://sigport.org/4696
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan, 2019. DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION. Available at: http://sigport.org/4696.
Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. (2019). "DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION." Web.
1. Yasmin M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, K. Palaniappan. DEEP U-NET REGRESSION AND HAND-CRAFTED FEATURE FUSION FOR ACCURATE BLOOD VESSEL SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4696

A dual attention dilated residual network for liver lesion classification and localization on CT images


Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Paper Details

Authors:
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu
Submitted On:
18 September 2019 - 1:55am
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[1] Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , "A dual attention dilated residual network for liver lesion classification and localization on CT images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4631. Accessed: Dec. 12, 2019.
@article{4631-19,
url = {http://sigport.org/4631},
author = {Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu },
publisher = {IEEE SigPort},
title = {A dual attention dilated residual network for liver lesion classification and localization on CT images},
year = {2019} }
TY - EJOUR
T1 - A dual attention dilated residual network for liver lesion classification and localization on CT images
AU - Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4631
ER -
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). A dual attention dilated residual network for liver lesion classification and localization on CT images. IEEE SigPort. http://sigport.org/4631
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , 2019. A dual attention dilated residual network for liver lesion classification and localization on CT images. Available at: http://sigport.org/4631.
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). "A dual attention dilated residual network for liver lesion classification and localization on CT images." Web.
1. Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . A dual attention dilated residual network for liver lesion classification and localization on CT images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4631

A dual attention dilated residual network for liver lesion classification and localization on CT images


Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Paper Details

Authors:
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu
Submitted On:
16 September 2019 - 1:10am
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[1] Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , "A dual attention dilated residual network for liver lesion classification and localization on CT images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4633. Accessed: Dec. 12, 2019.
@article{4633-19,
url = {http://sigport.org/4633},
author = {Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu },
publisher = {IEEE SigPort},
title = {A dual attention dilated residual network for liver lesion classification and localization on CT images},
year = {2019} }
TY - EJOUR
T1 - A dual attention dilated residual network for liver lesion classification and localization on CT images
AU - Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4633
ER -
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). A dual attention dilated residual network for liver lesion classification and localization on CT images. IEEE SigPort. http://sigport.org/4633
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , 2019. A dual attention dilated residual network for liver lesion classification and localization on CT images. Available at: http://sigport.org/4633.
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). "A dual attention dilated residual network for liver lesion classification and localization on CT images." Web.
1. Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . A dual attention dilated residual network for liver lesion classification and localization on CT images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4633

A dual attention dilated residual network for liver lesion classification and localization on CT images


Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Paper Details

Authors:
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu
Submitted On:
16 September 2019 - 1:10am
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[1] Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , "A dual attention dilated residual network for liver lesion classification and localization on CT images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4632. Accessed: Dec. 12, 2019.
@article{4632-19,
url = {http://sigport.org/4632},
author = {Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu },
publisher = {IEEE SigPort},
title = {A dual attention dilated residual network for liver lesion classification and localization on CT images},
year = {2019} }
TY - EJOUR
T1 - A dual attention dilated residual network for liver lesion classification and localization on CT images
AU - Xiao chen; Lanfen Lin;Dong Liang;Hongjie Hu;Qiaowei Zhang;Yutaro Iwamoto;Xian-Hua Han;Yen-Wei Chen;Ruofeng Tong;Jian Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4632
ER -
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). A dual attention dilated residual network for liver lesion classification and localization on CT images. IEEE SigPort. http://sigport.org/4632
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu , 2019. A dual attention dilated residual network for liver lesion classification and localization on CT images. Available at: http://sigport.org/4632.
Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . (2019). "A dual attention dilated residual network for liver lesion classification and localization on CT images." Web.
1. Xiao chen, Lanfen Lin,Dong Liang,Hongjie Hu,Qiaowei Zhang,Yutaro Iwamoto,Xian-Hua Han,Yen-Wei Chen,Ruofeng Tong,Jian Wu . A dual attention dilated residual network for liver lesion classification and localization on CT images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4632

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