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A Review on Prostate Cancer Detection using CNN

Merlyn Koonamparampath, Raj Shah, Mahipal Sundvesha, Meena Ugale
2022 International Journal for Research in Applied Science and Engineering Technology  
Keywords: Convolutional Neural Network, Deep Learning, Prostate Cancer Detection, Artificial Intelligence, Survey.  ...  We give a review of the usage of CNN applied to several automatic processing tasks of prostate cancer detection and diagnosis, to provide an overview of the progress in this field, based on the increased  ...  Li developed a deep learning architecture called XmasNet based on Convolutional neural networks. The system made use of the 3D multiparametric MRI data provided by the PROSTATEx challenge.  ... 
doi:10.22214/ijraset.2022.40747 fatcat:inl6l6uaofcevinxyzlyilvbjm

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume using a Base  ...  04 Bladder cancer treatment response assessment using deep learning in CT with transfer learning 10134 05 Convolutional neural network based deep-learning architecture for prostate cancer detection on  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

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  
Research on cancer molecular images using deep learning techniques is also increasing dynamically.  ...  We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging.  ...  Deep Learning in Cancer Classification For early detection of prostate cancer, deep learning techniques such as CNN and stacked autoencoders (SAE) have been applied on diffusion-weighted magnetic resonance  ... 
doi:10.1155/2017/9512370 pmid:29114182 pmcid:PMC5661078 fatcat:ev3zrlx67vfo5mt23e5u3y2t64

Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

Zia Khan, Norashikin Yahya, Khaled Alsaih, Mohammed Isam Al-Hiyali, Fabrice Meriaudeau
2021 IEEE Access  
Then original and augmented images 292 are used for training the deep convolutional neural network 293 (DCNN).  ...  Mun et al. [109] developed baseline convolutional 680 neural network (BCNN) with a residual feature for FIGURE 5 : 5 Categories of deep learning and machine learning techniques for prostate segmentation  ... 
doi:10.1109/access.2021.3090825 fatcat:l2xe2tdwk5b6ldn7axvzbp5a5a

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Michelle D. Bardis, Roozbeh Houshyar, Peter D. Chang, Alexander Ushinsky, Justin Glavis-Bloom, Chantal Chahine, Thanh-Lan Bui, Mark Rupasinghe, Christopher G. Filippi, Daniel S. Chow
2020 Cancers  
Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification.  ...  Prostate carcinoma is one of the most prevalent cancers worldwide.  ...  Although various DL architectures exist, convolutional neural networks (CNN) are considered well suited for medical imaging.  ... 
doi:10.3390/cancers12051204 pmid:32403240 pmcid:PMC7281682 fatcat:bnyww6wcbvhtxo2hkoiyw7ofne

Front Matter: Volume 10572

Jorge Brieva, Juan David García, Natasha Lepore, Eduardo Romero
2017 13th International Conference on Medical Information Processing and Analysis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume using a Base  ...  on multiparametric MRI [10572-4] 10572 14 Quantification of dose uncertainties for the bladder in prostate cancer radiotherapy based on dominant eigenmodes [10572-7] 10572 15 Improvement of Bragg  ... 
doi:10.1117/12.2310208 dblp:conf/sipaim/X17 fatcat:5mpaoft6nfcwrbrauhmpsgzemy

Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI

Paulo Lapa, Ivo Gonçalves, Leonardo Rundo, Mauro Castelli
2019 Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19  
In the clinical practice, multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, aiming at defining biomarkers for PCa.  ...  In the latest years, deep learning techniques have boosted the performance in prostate MR image analysis and classification.  ...  In the case of PCa detection and diagnosis on multiparametric MRI, end-to-end deep learning models were proposed and tested on the PROSTATEx dataset [2] .  ... 
doi:10.1145/3319619.3326864 dblp:conf/gecco/LapaGRC19a fatcat:qppg3erkaze27eqn7unoaietgq

Front Matter: Volume 11597

Karen Drukker, Maciej A. Mazurowski
2021 Medical Imaging 2021: Computer-Aided Diagnosis  
with and without computerized decision support 11597 1L MRI-based prostate and dominant lesion segmentation using deep neural network 11597 1M Clinically significant prostate cancer detection on MRI  ...  -42] 11597 1A Dense-layer-based YOLO-v3 for detection and localization of colon perforations [11597-43] 11597 1B Deep attention mask regional convolutional neural network for head-and-neck MRI multi  ...  segmentation of small metastatic brain tumors using liquid state machine ensemble 11597 2M Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with  ... 
doi:10.1117/12.2595447 fatcat:u25cvo7adbgcxb363rsnsgnsju

A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI

Quan Chen, Shiliang Hu, Peiran Long, Fang Lu, Yujie Shi, Yunpeng Li
2019 Technology in Cancer Research and Treatment  
In this work, we proposed a transfer learning-based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images.  ...  In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue  ...  Authors also would like to thank Drs Xiao Li and Qing Zou from Affiliated Cancer Hospital of Nanjing Medical University for valuable discussion.  ... 
doi:10.1177/1533033819858363 pmid:31221034 pmcid:PMC6589968 fatcat:dipyu7nb3bff5n74azltq4ld44

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks

Chih-Ching Lai, Hsin-Kai Wang Fu-Nien Wang, Yu-Ching Peng, Tzu-Ping Lin, Hsu-Hsia Peng, Shu-Huei Shen
2021 Sensors  
Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model.  ...  The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI).  ...  We would like to pay our gratitude and respect to him for his dedication to this research. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21082709 pmid:33921451 pmcid:PMC8070192 fatcat:cpv3l6gw2zbehlo7noikojtwmi

Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection [article]

Audrey G. Chung, Mohammad Javad Shafiee, Devinder Kumar, Farzad Khalvati, Masoom A. Haider, Alexander Wong
2015 arXiv   pre-print
As imaging-based prostate cancer screening, such as magnetic resonance imaging (MRI), requires an experienced medical professional to extensively review the data and perform a diagnosis, radiomics-driven  ...  We evaluated the performance of the discovered radiomic sequencer against a state-of-the-art hand-crafted radiomic sequencer for computer-aided prostate cancer detection with a feedforward neural network  ...  The use of magnetic resonance imaging (MRI) has recently grown in popularity as a non-invasive imaging-based prostate cancer detection method; however, a diagnosis through MRI requires an experienced medical  ... 
arXiv:1509.00111v3 fatcat:ysou3y4m5bbeliq7o7nz2t7mli

Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks

Tyler Clark, Junjie Zhang, Sameer Baig, Alexander Wong, Masoom A. Haider, Farzad Khalvati
2017 Journal of Medical Imaging  
Prostate cancer is a leading cause of cancer-related death among men. Multiparametric magnetic resonance imaging has become an essential part of the diagnostic evaluation of prostate cancer.  ...  We present a fully automatic algorithm for delineation of the prostate gland and TZ in diffusion-weighted imaging (DWI) via a stack of fully convolutional neural networks.  ...  Conclusions We presented an approach based on multiple convolutional neural networks that perform automated classification and segmentation of MRI prostate images in DWI.  ... 
doi:10.1117/1.jmi.4.4.041307 pmid:29057288 pmcid:PMC5644511 fatcat:zrnlnret6zbyxigc5ffwcd7vsa

Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

Piotr Sobecki, Rafał Jóźwiak, Katarzyna Sklinda, Artur Przelaskowski
2021 PeerJ  
, interpretation, and reporting of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations.  ...  Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research.  ...  Convolution is a powerful concept for constructing a robust feature space based on image data.  ... 
doi:10.7717/peerj.11006 pmid:33732553 pmcid:PMC7953869 fatcat:tpfzqhc57zbyhhleazgb5c26mi

Application of U-Net based Multiparameter Magnetic Resonance Image Fusion in the Diagnosis of Prostate Cancer

Xunan Huang, Bo Zhang, Xiaoling Zhang, Min Tang, Qiguang Miao, Tanping Li, Guang Jia
2021 IEEE Access  
Through neural network learning, a weight distribution is generated based on the relationship between the image feature information and the multifocus training target.  ...  The MRI image pair of prostate cancer (axial T2-weighted and ADC map) is fused using a strategy based on local similarity and Gaussian pyramid transformation.  ...  Image fusion based on deep learning has been widely used in multiparametric medical imaging [25] , infrared and visible imaging [26] , and remote sensing imaging [27] .  ... 
doi:10.1109/access.2021.3061078 fatcat:epnld57ngvab5cifdbnet6ffgm

Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer [article]

Dejan Kostyszyn, Tobias Fechter, Nico Bartl, Anca L. Grosu, Christian Gratzke, August Sigle, Michael Mix, Juri Ruf, Thomas F. Fassbender, Selina Kiefer, Alisa S. Bettermann, Nils H. Nicolay (+13 others)
2020 arXiv   pre-print
The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET.  ...  Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa).  ...  With the rise of deep learning in the recent years convolutional neural networks (CNNs) based algorithms achieved remarkable results handling this task.  ... 
arXiv:2008.03201v1 fatcat:3yi3dc6hxzabjmubwgeos4p6km
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