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PROSTATE ULTRASOUND IMAGE CLASSIFICATION USING CNN-BILSTM

Ramesh J., Dr. Manavalan R.
2021 Indian Journal of Computer Science and Engineering  
In this article, Convolutional Neural Network with Bi-directional Long Short Term Memory (CNN-BiLSTM) is proposed for the classification task to distinguish the normal from abnormal image of prostate gland  ...  Image classification task plays a vital role in the Computer-Aided Diagnosis system for diagnosis of prostate cancer.  ...  LSTM networks are a kind of recurrent neural network fit for learning request dependence in sequence prediction problems.  ... 
doi:10.21817/indjcse/2021/v12i6/211206028 fatcat:6pvwjbl3lbci5hjiyeq4hi7pty

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
345 Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase  ...  Constraint 639 How to Cure Cancer with Unpaired Image Translation 645 3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection 646 A Comprehensive Approach for Learning-based  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Front Matter: Volume 10140

Proceedings of SPIE, Metin N. Gurcan, John E. Tomaszewski
2017 Medical Imaging 2017: Digital Pathology  
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.  ...  14] 10140 0G Automatic computational labeling of glomerular textural boundaries [10140-15] 10140 0H Convolutional neural networks for prostate cancer recurrence prediction [10140-16] SESSION 4  ...  Wagner Best Student Paper Award for Digital Pathology (10140) Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification  ... 
doi:10.1117/12.2270372 dblp:conf/midp/X17 fatcat:6yeb63ix6bau7jhipthayjih24

Transfer Learning with Edge Attention for Prostate MRI Segmentation [article]

Xiangxiang Qin
2019 arXiv   pre-print
In this paper, we propose a trans-fer learning method based on deep neural network for prostate MRI segmenta-tion.  ...  If prostate cancer can be found as early as possible and treated in time, it will have a high survival rate. Therefore, it is of great significance for the diagnosis and treatment of prostate cancer.  ...  Therefore, it is of great significance for the diagnosis and treatment of prostate cancer.  ... 
arXiv:1912.09847v1 fatcat:2uaaf5nzefbsfiehjfkshn2mha

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library.  ...  risk prediction using a new CAD-based region segmentation scheme [10575-24] 10575 0Q Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Development of Conditional Random Field Insert for UNet-based Zonal Prostate Segmentation on T2-Weighted MRI [article]

Peng Cao and Susan M. Noworolski and Olga Starobinets and Natalie Korn and Sage P. Kramer and Antonio C. Westphalen and Andrew P. Leynes and Valentina Pedoia and Peder Larson
2020 arXiv   pre-print
Purpose: A conventional 2D UNet convolutional neural network (CNN) architecture may result in ill-defined boundaries in segmentation output.  ...  Conclusion: UNet based deep neural networks demonstrated in this study can perform zonal prostate segmentation, achieving high Dice coefficients compared with those in the literature.  ...  Figure 3 shows typical neural network segmentation results from 4 participants. The neural network was able to predict the correct zonal boundary in most cases.  ... 
arXiv:2002.06330v1 fatcat:7jhsy4ec2ngeddlahfexfkizy4

Artificial Intelligence in Lung Cancer Pathology Image Analysis

Shidan Wang, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, Junya Fujimoto, Hongyu Liu, John Minna, Ignacio Ivan Wistuba, Yang Xie, Guanghua Xiao
2019 Cancers  
We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.  ...  Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.  ...  Acknowledgments: The authors thank Jessie Norris for helping us to edit this manuscript.  ... 
doi:10.3390/cancers11111673 pmid:31661863 pmcid:PMC6895901 fatcat:bntqqbilwrbybdhgfd73px5zki

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Yang Han, Zhengguo Han, Jianhui Wu, Yanlong Yu, Shuqing Gao, Dianbo Hua, Aimin Yang
2020 IEEE Access  
In view of the uncertainty of the cause and time of recurrence of cancer patients, the convolutional neural network algorithm was used to predict both of them.  ...  To solve the problem of the optimal nutrition program for the rehabilitation of cancer patients, we took the recurrence time as the objective function, and established the recommendation model of the optimal  ...  The convolutional neural network model of cancer recovery prediction is recommended for the engine.  ... 
doi:10.1109/access.2020.2978078 fatcat:3oab23wufra4jm3enxzdpg27ra

Visualization for Histopathology Images using Graph Convolutional Neural Networks [article]

Mookund Sureka, Abhijeet Patil, Deepak Anand, Amit Sethi
2020 arXiv   pre-print
Our visualization of such networks trained to distinguish between invasive and in-situ breast cancers, and Gleason 3 and 4 prostate cancers generate interpretable visual maps that correspond well with  ...  In histology, in particular, while there is rich detail available at the cellular level and that of spatial relationships between cells, it is difficult to modify convolutional neural networks to point  ...  CNNs have also been used on histopathology images for tasks such as screening pre-cancerous lesions and localizing tumors [4] , as well as predicting mutations [5] , survival [6] , and cancer recurrence  ... 
arXiv:2006.09464v1 fatcat:wsl6hggfzveo3drhnu26yxmfyu

A Systematic Review of Artificial Intelligence in Prostate Cancer

Derek J Van Booven, Manish Kuchakulla, Raghav Pai, Fabio S Frech, Reshna Ramasahayam, Pritika Reddy, Madhumita Parmar, Ranjith Ramasamy, Himanshu Arora
2021 Research and Reports in Urology  
Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.  ...  The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making.  ...  Dipen J Parekh, Dr Joshua M Hare) for their insights, suggestions, and support during this study.  ... 
doi:10.2147/rru.s268596 pmid:33520879 pmcid:PMC7837533 fatcat:a2eecsaqmza47lmubnfsahs6wq

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Khushboo Munir, Hassan Elahi, Afsheen Ayub, Fabrizio Frezza, Antonello Rizzi
2019 Cancers  
Boltzmann's machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN  ...  ), multi-instance learning convolutional neural network (MIL-CNN).  ...  Recurrent Neural Networks (RNNs) Recurrent neural networks are a powerful model of sequential data [126] .  ... 
doi:10.3390/cancers11091235 pmid:31450799 pmcid:PMC6770116 fatcat:ktuuttdu6zc7phj3mahp5yynxq

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  
Öcal et al. [140] fused the Nested 3D dimensional volumetric convolutional neural network (Nested-Vnet3d) and 2D volumetric convolutional neural network (V-net2d) for segmentation of prostate 835 trained  ...  Then original and augmented images 292 are used for training the deep convolutional neural network 293 (DCNN).  ... 
doi:10.1109/access.2021.3090825 fatcat:l2xe2tdwk5b6ldn7axvzbp5a5a

Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Leo Benning, Andreas Peintner, Lukas Peintner
2022 Cancers  
Despite the efforts of the past decades, cancer is still among the key drivers of global mortality.  ...  Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.  ...  Acknowledgments: We thank the Bayer Foundation for enabling this work by a StartUp Grant to L.P. The work of A.P. is funded by the Austrian Science Fund FWF P33526.  ... 
doi:10.3390/cancers14030623 pmid:35158890 pmcid:PMC8833439 fatcat:hdf7qt7wj5elndhrdzv43sjk5e

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

Eirini Arvaniti, Kim S. Fricker, Michael Moret, Niels Rupp, Thomas Hermanns, Christian Fankhauser, Norbert Wey, Peter J. Wild, Jan H. Rüschoff, Manfred Claassen
2018 Scientific Reports  
This work was sponsored in part by a grant from the Swiss National Science Foundation (zBioLink), a SystemsX. ch grant (Phosphonet-Personalized Precision Medicine) and a grant provided by the Foundation for  ...  Prior work on the analysis of prostate cancer digital pathology images includes detection of cancerous tissue 18 , prediction of SPOP mutation status 20 and of cancer recurrence 21 , as well as tissue  ...  Results In this study, we focus on a well annotated dataset of prostate cancer tissue microarrays 27 and demonstrate that a convolutional neural network can be successfully trained as a Gleason score  ... 
doi:10.1038/s41598-018-30535-1 pmid:30104757 pmcid:PMC6089889 fatcat:yc7jz5nei5bxpguiq6iriyqzwe

ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE
ШТУЧНИЙ ІНТЕЛЕКТ ТА ПАТОЛОГІЯ НАСТУПНОГО ПОКОЛІННЯ: ШЛЯХ ДО ПЕРСОНАЛІЗОВАНОЇ МЕДИЦИНИ

Oleksandr Dudin, Shupyk National University of Healthcare of Ukraine, Ozar Mintser, Oksana Sulaieva, Shupyk National University of Healthcare of Ukraine, Shupyk National University of Healthcare of Ukraine
2021 Proceedings of the Shevchenko Scientific Society. Medical Sciences  
Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was  ...  In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity  ...  Їх Recurrent neural network, RNN), які зазвичай  ... 
doi:10.25040/ntsh2021.02.07 fatcat:zhbpzkwbafemriuqj6ujmlikz4
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