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Medical image analysis

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

ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET

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Authors:
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang
Submitted On:
12 September 2019 - 6:38am
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[1] Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang, "ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4603. Accessed: Sep. 30, 2020.
@article{4603-19,
url = {http://sigport.org/4603},
author = {Shaobo Min; Xuejin Chen; Hongtao Xie; Zheng-Jun Zha; Guoqiang Bi; Feng Wu; Yongdong Zhang },
publisher = {IEEE SigPort},
title = {ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET},
year = {2019} }
TY - EJOUR
T1 - ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET
AU - Shaobo Min; Xuejin Chen; Hongtao Xie; Zheng-Jun Zha; Guoqiang Bi; Feng Wu; Yongdong Zhang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4603
ER -
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. (2019). ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET. IEEE SigPort. http://sigport.org/4603
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang, 2019. ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET. Available at: http://sigport.org/4603.
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. (2019). "ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET." Web.
1. Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4603

Two-stage Unsupervised Learning Method for Affine and Deformable Registration


Conventional medical image registration relies on time-consuming iterative optimization. We propose a two-stage unsupervised learning method for 3D medical image registration. In the first stage, we learn a global image-wise affine map by a deep network. In the second stage, we learn a local voxel-wise deformation vector field by an encoder-decoder architecture. The final registered image is acquired by applying the local deformation field to the moved image of the first stage.

Paper Details

Authors:
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan
Submitted On:
12 September 2019 - 1:32am
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[1] Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan, "Two-stage Unsupervised Learning Method for Affine and Deformable Registration", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4600. Accessed: Sep. 30, 2020.
@article{4600-19,
url = {http://sigport.org/4600},
author = {Dongdong Gu; Guocai Liu; Juanxiu Tian; Qi Zhan },
publisher = {IEEE SigPort},
title = {Two-stage Unsupervised Learning Method for Affine and Deformable Registration},
year = {2019} }
TY - EJOUR
T1 - Two-stage Unsupervised Learning Method for Affine and Deformable Registration
AU - Dongdong Gu; Guocai Liu; Juanxiu Tian; Qi Zhan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4600
ER -
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. (2019). Two-stage Unsupervised Learning Method for Affine and Deformable Registration. IEEE SigPort. http://sigport.org/4600
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan, 2019. Two-stage Unsupervised Learning Method for Affine and Deformable Registration. Available at: http://sigport.org/4600.
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. (2019). "Two-stage Unsupervised Learning Method for Affine and Deformable Registration." Web.
1. Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. Two-stage Unsupervised Learning Method for Affine and Deformable Registration [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4600

TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION


A volumetric non-blind single image super-resolution technique using tensor factorization has been recently introduced by our group. That method allowed a 2-order-of-magnitude faster high-resolution image reconstruction with equivalent image quality compared to state-of-the-art algorithms. In this work a joint alternating recovery of the high-resolution image and of the unknown point spread function parameters is proposed. The method is evaluated on dental computed tomography images.

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Authors:
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé
Submitted On:
11 September 2019 - 12:17pm
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[1] Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé, "TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4595. Accessed: Sep. 30, 2020.
@article{4595-19,
url = {http://sigport.org/4595},
author = {Janka Hatvani; Adrian Basarab; Jérome Michetti; Miklós Gyöngy; Denis Kouamé },
publisher = {IEEE SigPort},
title = {TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION},
year = {2019} }
TY - EJOUR
T1 - TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION
AU - Janka Hatvani; Adrian Basarab; Jérome Michetti; Miklós Gyöngy; Denis Kouamé
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4595
ER -
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. (2019). TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION. IEEE SigPort. http://sigport.org/4595
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé, 2019. TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION. Available at: http://sigport.org/4595.
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. (2019). "TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION." Web.
1. Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4595

Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach


In medical practice, the X-ray Computed tomography-based scans expose a high radiation dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose CT scan can reduce radiation exposure to the patient. But the reduced radiation dose degrades image quality for human perception, and adversely affects the radiologist’s diagnosis and prognosis. In this paper, we introduce a GAN based auto-encoder network to de-noise the CT images.

Paper Details

Authors:
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta
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24 September 2019 - 10:23am
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Low Dose Image Reconstruction

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[1] Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta, "Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4585. Accessed: Sep. 30, 2020.
@article{4585-19,
url = {http://sigport.org/4585},
author = {Shiba Kuanar; Vassilis Athitsos; Dwarikanath Mahapatra; K.R. Rao; Zahid Akhtar; Dipankar Dasgupta },
publisher = {IEEE SigPort},
title = {Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach},
year = {2019} }
TY - EJOUR
T1 - Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach
AU - Shiba Kuanar; Vassilis Athitsos; Dwarikanath Mahapatra; K.R. Rao; Zahid Akhtar; Dipankar Dasgupta
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4585
ER -
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. (2019). Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach. IEEE SigPort. http://sigport.org/4585
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta, 2019. Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach. Available at: http://sigport.org/4585.
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. (2019). "Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach." Web.
1. Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4585

COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS

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10 September 2019 - 9:57pm
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[1] , "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4580. Accessed: Sep. 30, 2020.
@article{4580-19,
url = {http://sigport.org/4580},
author = { },
publisher = {IEEE SigPort},
title = {COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS},
year = {2019} }
TY - EJOUR
T1 - COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4580
ER -
. (2019). COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. IEEE SigPort. http://sigport.org/4580
, 2019. COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. Available at: http://sigport.org/4580.
. (2019). "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS." Web.
1. . COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4580

Nuclei Segmentation in Histopathology Images


Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming \textit{traditional} approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms.

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Authors:
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard
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16 May 2019 - 11:13am
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[1] Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, "Nuclei Segmentation in Histopathology Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4539. Accessed: Sep. 30, 2020.
@article{4539-19,
url = {http://sigport.org/4539},
author = {Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard },
publisher = {IEEE SigPort},
title = {Nuclei Segmentation in Histopathology Images},
year = {2019} }
TY - EJOUR
T1 - Nuclei Segmentation in Histopathology Images
AU - Deniz Mercadier Sayin; Beril Besbinar; Pascal Frossard
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4539
ER -
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). Nuclei Segmentation in Histopathology Images. IEEE SigPort. http://sigport.org/4539
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard, 2019. Nuclei Segmentation in Histopathology Images. Available at: http://sigport.org/4539.
Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. (2019). "Nuclei Segmentation in Histopathology Images." Web.
1. Deniz Mercadier Sayin, Beril Besbinar, Pascal Frossard. Nuclei Segmentation in Histopathology Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4539

A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks


Lung cancer is the most prevalent cancer worldwide with about 230,000 new cases every year. Most cases go undiagnosed until it’s too late, especially in developing countries and remote areas. Early detection is key to beating cancer. Towards this end, the work presented here proposes an automated pipeline for lung tumor detection and segmentation from 3D lung CT scans from the NSCLC Radiomics Dataset. It also presents a new dilated hybrid-3D convolutional neural network architecture for tumor segmentation. First, a binary classifier chooses CT scan slices that may contain parts of a tumor.

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Authors:
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque
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16 May 2019 - 8:05am
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LungNet3D-Poster

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[1] Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4482. Accessed: Sep. 30, 2020.
@article{4482-19,
url = {http://sigport.org/4482},
author = {Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque },
publisher = {IEEE SigPort},
title = {A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks
AU - Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4482
ER -
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4482
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, 2019. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. Available at: http://sigport.org/4482.
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks." Web.
1. Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4482

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