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Automated Gleason Grading and Gleason Pattern Region Segmentation Based on Deep Learning for Pathological Images of Prostate Cancer

Yuchun Li, Mengxing Huang, Yu Zhang, Jing Chen, Haixia Xu, Gang Wang, Wenlong Feng
2020 IEEE Access  
Our study shows that the method of combining different deep neural network architectures is suitable for more objective and reproducible Gleason grading of prostate cancer.  ...  The quantitative experiments on 1211 prostate cancer tissue microarrays demonstrate that our results have a high correlation with the manual segmentations.  ...  [43] proposed an automatic visual inspection of the whole slide images detection method based on deep learning frameworks for high grade Gleason score. Li et al.  ... 
doi:10.1109/access.2020.3005180 fatcat:pghwqk26nzgshm3uwz2y5mxkwm

Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks [article]

Jingwen Wang, Richard J. Chen, Ming Y. Lu, Alexander Baras, Faisal Mahmood
2019 arXiv   pre-print
In this work, we propose a weakly-supervised approach for grade classification in tissue micro-arrays (TMA) using graph convolutional networks (GCNs), in which we model the spatial organization of cells  ...  In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands.  ...  neural net- pathology: Challenges and promises for tissue analysis,” Com- works for an automatic classification of prostate tissue slides puterized Medical Imaging and Graphics, vol.  ... 
arXiv:1910.13328v2 fatcat:cxhdf6ipjzahphg2p66mkj3aqe

Large scale digital prostate pathology image analysis combining feature extraction and deep neural network [article]

Naiyun Zhou, Andrey Fedorov, Fiona Fennessy, Ron Kikinis, Yi Gao
2017 arXiv   pre-print
The algorithm is tested on 368 whole slide images from the TCGA data set and achieves an overall accuracy of 75% in differentiating Gleason 3+4 with 4+3 slides.  ...  In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction.  ...  Convolutional Neural Network Convolutional Neural Network (CNN) has achieved tremendous success in computer vision applications.  ... 
arXiv:1705.02678v2 fatcat:vcxvgxwd65avndkovtdu55x3iy

Automated Gleason grading of prostate cancer tissue microarrays via deep learning [article]

Eirini Arvaniti, Kim S. Fricker, Michael Moret, Niels J. Rupp, Thomas Hermanns, Christian Fankhauser, Norbert Wey, Peter J. Wild, Jan H. Rueschoff, Manfred Claassen
2018 bioRxiv   pre-print
In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining.  ...  The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960's.  ...  Research in Science and the Humanities at the University of Zurich provided to PJW.  ... 
doi:10.1101/280024 fatcat:hobeu5zs6ngy5eo3h5rdgmdady

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.  ...  These two-number sets start with 00, 01, 02, 03, 04,  ...  images through large scale image synthesis [10140-19] POSTER SESSION 10140 0M 10140 0O Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason  ... 
doi:10.1117/12.2270372 dblp:conf/midp/X17 fatcat:6yeb63ix6bau7jhipthayjih24

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  
Research in Science and the Humanities at the University of Zurich provided to PJW.  ...  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  ...  The final classification layer was replaced with an equivalent convolutional layer with four output channels, one for each Gleason class.  ... 
doi:10.1038/s41598-018-30535-1 pmid:30104757 pmcid:PMC6089889 fatcat:yc7jz5nei5bxpguiq6iriyqzwe

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies

Marit Lucas, Ilaria Jansen, C. Dilara Savci-Heijink, Sybren L. Meijer, Onno J. de Boer, Ton G. van Leeuwen, Daniel M. de Bruin, Henk A. Marquering
2019 Virchows Archiv  
Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level.  ...  With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible.  ...  Authors' contributions ML, IJ, HAM, and DMdB were involved with the conception and design of the study, as well as the analysis and interpretation of the data.  ... 
doi:10.1007/s00428-019-02577-x fatcat:saipkrwdu5c5hjdvfqyqwdtiae

WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images

Julio Silva-Rodríguez, Adrián Colomer, Valery Naranjo
2021 Computerized Medical Imaging and Graphics  
Prostate cancer is one of the main diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic tool for prostate cancer.  ...  We compared the model performance for semantic segmentation of Gleason grades with supervised state-of-the-art architectures in the test cohort.  ...  Müller, Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, Medical Imaging 2017: Digital Pathology 10140 (2017) 101400O.  ... 
doi:10.1016/j.compmedimag.2020.101846 pmid:33485056 fatcat:3466aib7efbjthc6x7es75ottm

Microscopic medical image classification framework via deep learning and shearlet transform

Hadi Rezaeilouyeh, Ali Mollahosseini, Mohammad H. Mahoor
2016 Journal of Medical Imaging  
A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented.  ...  An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features.  ...  Kourosh Jafari-Khouzani for sharing his code and dataset with us.  ... 
doi:10.1117/1.jmi.3.4.044501 pmid:27872871 pmcid:PMC5093219 fatcat:ubsfbu3cdzez3crupodwxelily

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images [chapter]

Jian Ren, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran, Xin Qi
2018 Lecture Notes in Computer Science  
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis.  ...  We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.  ...  Figure 3a shows an example prostate WSI from CINJ with the high Gleason grade (Gleason score 8) and the ground-truth heatmap overlaid on it.  ... 
doi:10.1007/978-3-030-00934-2_23 pmid:30465047 pmcid:PMC6241308 fatcat:l73cakjxabeqjodg5wpypzwmhe

Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection

Julio Silva-Rodríguez, Adrián Colomer, María A. Sales, Rafael Molina, Valery Naranjo
2020 Computer Methods and Programs in Biomedicine  
The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns based on the Gleason grading system.  ...  Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification.  ...  Taking as input a prostate whole slide image (WSI), the system performs a patch-level Gleason grade prediction through convolutional neural networks.  ... 
doi:10.1016/j.cmpb.2020.105637 pmid:32653747 fatcat:txoi2flhjrdidkyiqmzmdrkfry

Automated Detection of Cribriform Growth Patterns in Prostate Histology Images [article]

Pierre Ambrosini, Eva Hollemans, Charlotte F. Kweldam, Geert J. L. H. van Leenders, Sjoerd Stallinga, Frans Vos
2020 arXiv   pre-print
To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies.  ...  Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically.  ...  Conclusion We proposed a convolutional neural network to automatically detect and localize cribriform growth patterns in prostate biopsy images.  ... 
arXiv:2003.10543v1 fatcat:gezituyw7je7tnsmmd67t3diky

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images [article]

Jian Ren, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran, Xin Qi
2018 arXiv   pre-print
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis.  ...  We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.  ...  Figure 3a shows an example prostate WSI from CINJ with the high Gleason grade (Gleason score 8) and the ground-truth heatmap overlaid on it.  ... 
arXiv:1806.01357v2 fatcat:xpkpyogzknb5tctd6rcnin7lpe

Improving Prostate Cancer Detection with Breast Histopathology Images [article]

Umair Akhtar Hasan Khan, Carolin Stürenberg, Oguzhan Gencoglu, Kevin Sandeman, Timo Heikkinen, Antti Rannikko, Tuomas Mirtti
2019 arXiv   pre-print
With the help of transfer learning, classification and segmentation performance of neural network models have been further increased.  ...  Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images.  ...  Such an approach has been utilized by different works [11, 18] . For Gleason grading, Nagpal et al.  ... 
arXiv:1903.05769v1 fatcat:flpb7sd2kzfjdozaqj6s47zfaa

DiagSet: a dataset for prostate cancer histopathological image classification [article]

Michał Koziarski, Bogusław Cyganek, Bogusław Olborski, Zbigniew Antosz, Marcin Żydak, Bogdan Kwolek, Paweł Wąsowicz, Andrzej Bukała, Jakub Swadźba, Piotr Sitkowski
2021 arXiv   pre-print
The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition, and is compared in  ...  Cancer diseases constitute one of the most significant societal challenges. In this paper we introduce a novel histopathological dataset for prostate cancer detection.  ...  of histopathological images" supported by the National Center for Research and Development: grant no.  ... 
arXiv:2105.04014v1 fatcat:66bfdmio4jdbbbb3puf6xyfttq
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