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Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni Nesrine et. al.
2020 International Journal of Computing and Digital Systems  
Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer.  ...  point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-  ...  Pelvic lymph-node classification In this paper, we use both fusion and segmentation results to improve the PLNs detection performance.  ... 
doi:10.12785/ijcds/090103 fatcat:wchlgtj6njd3jgno4ne7imfvzy

Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks

Benjamin H. Kann, Sanjay Aneja, Gokoulakrichenane V. Loganadane, Jacqueline R. Kelly, Stephen M. Smith, Roy H. Decker, James B. Yu, Henry S. Park, Wendell G. Yarbrough, Ajay Malhotra, Barbara A. Burtness, Zain A. Husain
2018 Scientific Reports  
We trained a 3-dimensional convolutional neural network using a dataset of 2,875 CT-segmented lymph node samples with correlating pathology labels, cross-validated and fine-tuned on 124 samples, and conducted  ...  Despite modern imaging techniques, there are certain radiographic features that remain difficult to detect by clinicians, including the presence of lymph node metastasis (NM), and of particular challenge  ...  We hypothesized that this would help the neural network focus on features unrelated to lymph node size.  ... 
doi:10.1038/s41598-018-32441-y pmid:30232350 pmcid:PMC6145900 fatcat:4sfcbrznebgvveochiarqzrdiq

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
2017 Contrast Media & Molecular Imaging  
Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection  ...  In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical  ...  Deep neural networks (DNN) were found effective for task-specific high-level feature learning [13] and thus were used to detect MRI brain-pathology-specific features by integrating information from multimodal  ... 
doi:10.1155/2017/9512370 pmid:29114182 pmcid:PMC5661078 fatcat:ev3zrlx67vfo5mt23e5u3y2t64

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Rima Hajjo, Dima A Sabbah, Sanaa K Bardaweel, Alexander Tropsha
2021 Diagnostics  
Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care.  ...  We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical  ...  , feature detection protocols, and classifiers are important factors in lung cancer prediction [55] Radiomic signature is significantly associated with lymph node (LN) status in colorectal cancer  ... 
doi:10.3390/diagnostics11050742 pmid:33919342 pmcid:PMC8143297 fatcat:d5k6655lbfamzhjleyso5q54u4

Classifying functional nuclear images with convolutional neural networks: a survey

Qiang Lin, Zhengxing Man, Yongchun Cao, Tao Deng, Chengcheng Han, Chuangui Cao, Linjun Zhang, Sitao Zeng, Ruiting Gao, Weilan Wang, Jinshui Ji, Xiaodi Huang
2020 IET Image Processing  
hybrid modalities with computed tomography and magnetic resonance imaging images by using convolutional neural network (CNN) techniques.  ...  Nuclear medicine functional imaging has been used to acquire information about areas of concerns (e.g. lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making  ...  A total of 400 lymph nodes in 136 patients were used for training while 164 lymph nodes in 49 patients were used for testing.  ... 
doi:10.1049/iet-ipr.2019.1690 fatcat:i4h7n4hsc5b2lenwmzekybxwte

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation [article]

Jose Dolz and Christian Desrosiers and Li Wang and Jing Yuan and Dinggang Shen and Ismail Ben Ayed
2017 arXiv   pre-print
This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input.  ...  in most metrics.  ...  Acknowledgments This work is supported by the National Science and Engineering Research Council of Canada (NSERC), discovery grant program, and by the ETS Research Chair on Artificial Intelligence in Medical  ... 
arXiv:1712.05319v2 fatcat:d5enbvv4lbhpxhetfaghecafyi

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 Journal of Magnetic Resonance Imaging  
We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology.  ...  Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems.  ...  Grant Support: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360. BIBLIOGRAPHY  ... 
doi:10.1002/jmri.26534 pmid:30575178 pmcid:PMC6483404 fatcat:7jg5sr7z6bbehd6xabsjw6bcde

Multimodal brain tumor classification [article]

Marvin Lerousseau, Eric Deutsh, Nikos Paragios
2020 arXiv   pre-print
Cancer is a complex disease that provides various types of information depending on the scale of observation.  ...  In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification.  ...  Finally, ensembling several neural networks is known to significantly improve the performance and robustness of a neural network system [6] .  ... 
arXiv:2009.01592v2 fatcat:2gbjbt5obnbbjgq2twskljjqwi

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library.  ...  The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  Contents 02 Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images 10575 03 Early detection of lung cancer recurrence after stereotactic ablative radiation therapy  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Radiomics of Multiparametric MRI to Predict Biochemical Recurrence of Localized Prostate Cancer After Radiation Therapy

Qiu-Zi Zhong, Liu-Hua Long, An Liu, Chun-Mei Li, Xia Xiu, Xiu-Yu Hou, Qin-Hong Wu, Hong Gao, Yong-Gang Xu, Ting Zhao, Dan Wang, Hai-Lei Lin (+4 others)
2020 Frontiers in Oncology  
The optimal feature set, identified through an Inception-Resnet v2 network, consisted of a combination of T1, T2, and diffusion-weighted imaging (DWI) MR series.  ...  Through a Wilcoxon sign rank test, a total of 45 distinct signatures were extracted from 1,536 radiomics features and used in our Adaboost model.  ...  status, and lymph node status).  ... 
doi:10.3389/fonc.2020.00731 pmid:32477949 pmcid:PMC7235325 fatcat:p2d5d7ada5atlbimvg54jvfv7q

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Eleftherios Trivizakis, Georgios Papadakis, Ioannis Souglakos, Nikolaos Papanikolaou, Lefteris Koumakis, Demetrios Spandidos, Aristidis Tsatsakis, Apostolos Karantanas, Kostas Marias
2020 International Journal of Oncology  
The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradigms that have led to significant advancements in information processing techniques in the context of  ...  The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications.  ...  Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges').  ... 
doi:10.3892/ijo.2020.5063 pmid:32467997 pmcid:PMC7252460 fatcat:dwgljcnhfzhllbfmb2p4ujtb3e

Deep learning in radiology: an overview of the concepts and a survey of the state of the art [article]

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 arXiv   pre-print
We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology.  ...  Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems.  ...  Acknowledgments: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360.  ... 
arXiv:1802.08717v1 fatcat:7qirj6hb2bdafnplc6au4wysqi

Feasibility of Deep Learning-Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images

Tyler J Bradshaw, Gengyan Zhao, Hyungseok Jang, Fang Liu, Alan B McMillan
2018 Tomography  
We used axial T2 and T1 LAVA Flex magnetic resonance imaging images that were acquired for diagnostic purposes as inputs to a 3D deep convolutional neural network.  ...  In evaluating 16 soft tissue lesions, the distribution of errors for maximum standardized uptake value was significantly narrower using deepMRAC (-1.0% ± 1.3%) than using system MRAC method (0.0% ± 6.4%  ...  , 5 lesions along the vaginal cuff, 1 cervical lesion, 1 ovarian lesion, 1 periaortic lymph node, and 1 pelvis sidewall lymph node.  ... 
doi:10.18383/j.tom.2018.00016 pmid:30320213 pmcid:PMC6173790 fatcat:yydz27wgbzeehpfhoqazitxovq

Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy

Shuanbao Yu, Jin Tao, Biao Dong, Yafeng Fan, Haopeng Du, Haotian Deng, Jinshan Cui, Guodong Hong, Xuepei Zhang
2021 BMC Urology  
All methods should continue to be explored and used in complementary ways.  ...  Results The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) yielded similar diagnostic accuracy with logistic regression, while classification and regression tree  ...  The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa (MRI-PCa), seminal vesicle invasion (MRI-SVI), lymph node invasion  ... 
doi:10.1186/s12894-021-00849-w pmid:33993876 pmcid:PMC8127331 fatcat:hl4msrf4g5aexdg6745w23w56a

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks.  ...  Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article.  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm
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