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Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

Convolutional neural networks: an overview and application in radiology

Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi
2018 Insights into Imaging  
Key Points • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including  ...  Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology  ...  Acknowledgements We would like to acknowledge Yasuhisa Kurata, MD, PhD, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine.  ... 
doi:10.1007/s13244-018-0639-9 pmid:29934920 fatcat:vbo6znqwjbax7h425choj2ikwm

Transformers in Medical Image Analysis: A Review [article]

Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen
2022 arXiv   pre-print
Our paper aims to promote awareness and application of Transformers in the field of medical image analysis.  ...  Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations.  ...  This work was also supported in part by the grant from Jiangsu Provincial Key R&D Program under No. BE2020620 and BE2020723.  ... 
arXiv:2202.12165v2 fatcat:wjeuwhcu5ngybcia5k7lyxntgi

Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes

Yuya Onishi, Atsushi Teramoto, Masakazu Tsujimoto, Tetsuya Tsukamoto, Kuniaki Saito, Hiroshi Toyama, Kazuyoshi Imaizumi, Hiroshi Fujita
2021
.: Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks. Int. J. Comput. Assist. Radiol.  ...  .: Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed. Res.  ... 
doi:10.11323/jjmp.41.3_155 pmid:34744128 fatcat:5cx5dmp54zf27ld3f6ylzion2m

Generation of Lung Nodule Using Generative Adversarial Networks and Its Application for AI-CAD
敵対的生成ネットワークによる肺結節CT画像の生成とAI-CADへの応用

Atsushi TERAMOTO
Medical Imaging and Information Sciences  
In this review article, we describe the generation of lung nodule images by generative adversarial networks and its application to AI-CADs as one of the solutions to this challenge.  ...  Many AI-CADs have been developed to assist physicians in the diagnosis of lung cancer using CT images.  ...  Tsujimoto, et al. : このように,2 段階の学習を経て作成された CNN は, Multiplanar analysis for pulmonary nodule classification 実画像のみを学習した CNN と比べて 10~30% ほど識別性 in CT images using deep convolutional neural network 能が向上することが  ... 
doi:10.11318/mii.38.57 fatcat:md53u33sn5cx3f7mguhrynbz7a

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Qin Pei, Yanan Luo, Yiyu Chen, Jingyuan Li, Dan Xie, Ting Ye
2022
The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis.  ...  Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems.  ...  Acknowledgments: We would like to thank the members in the research group who are not listed in the authors.  ... 
doi:10.1515/cclm-2022-0291 pmid:35771735 fatcat:s7i4juy3ffbxxigdq5rbiabohi

2021 AIUM Award Winners

2021 Journal of ultrasound in medicine  
This study aims to develop a convolutional neural network (CNN) for uniformity artifact classification from median clinical images.  ...  We designed an Atheromatic 2.0 system consisting of 3 kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning  ...  Optimal pain control for patients with traumatic rib fractures allows patients to participate in pulmonary hygiene, lung recruitment, deep breathing, and rehab without overly sedating which may result  ... 
doi:10.1002/jum.15752 fatcat:v4nx5fvjwndrzfppaiaylgon64

Machine learning for magnetic resonance image reconstruction and analysis

Chen Qin, Daniel Rueckert
2020
A convolutional recurrent neural network architecture (CRNN-MRI) is developed, where it embeds the iterative optimisation process in a learning setting and exploits the temporal redundancies of cardiac  ...  Finally, deep learning approaches for image registration and its applications are investigated.  ...  Convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs) are some of the most popular deep learning models that have been actively researched and  ... 
doi:10.25560/79301 fatcat:bdyyeutojjas3cppkur6xn6aqe