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Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
[article]
2020
arXiv
pre-print
Preserved Fulltext
The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. ...
Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. ...
METHODS The Gleason pattern grading of prostate images is addressed in this work by the pixel-level semantic segmentation using different convolutional-neural-networks models. ...
arXiv:2005.11368v1
fatcat:6vdio475cjchnjwgerkrcksjba
WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
2021
Computerized Medical Imaging and Graphics
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2021 pre-print arXiv:2105.10445v1 fatcat:r53zubcezjaihfjfu3wunhbkoq
The methodological core of this work is the proposed weakly-supervised-trained convolutional neural network, WeGleNet, based on a multi-class segmentation layer after the feature extraction module, a global-aggregation ...
We compared the model performance for semantic segmentation of Gleason grades with supervised state-of-the-art architectures in the test cohort. ...
In this work we propose a deep-learning architecture based on convolutional
neural networks able to perform a semantic segmentation of the Gleason grades
(i.e. non-cancerous tissue, GG3, GG4 or GG5 classes ...
doi:10.1016/j.compmedimag.2020.101846
pmid:33485056
fatcat:3466aib7efbjthc6x7es75ottm
Automated Gleason Grading and Gleason Pattern Region Segmentation Based on Deep Learning for Pathological Images of Prostate Cancer
2020
IEEE Access
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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. ...
This paper presents an automated Gleason grading and Gleason pattern region segmentation method based on deep learning for pathological images of prostate cancer. ...
Semantic segmentation uses FCN [28] , and most neural networks are based on it, but FCN has a big problem with segmenting images of prostate cancer TMAs. ...
doi:10.1109/access.2020.3005180
fatcat:pghwqk26nzgshm3uwz2y5mxkwm
A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies
2018
AMIA Annual Symposium Proceedings
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Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. ...
(stroma, benign glands, prostate cancer), outperforming other methods. ...
Semantic Image Segmentation with a Deep Convolutional Neural Network For baseline comparison, a deep CNN model was trained to produce pixel-wise class predictions. ...
pmid:29854182
pmcid:PMC5977596
fatcat:dwrg5qi2gbdnll3k5jjhhyaogu
Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer
2021
Journal of Healthcare Engineering
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This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk ...
A total of 89 patients with prostate cancer and benign prostatic hyperplasia diagnosed by MRI examination and pathological examination in hospital were selected as the research objects in this study (they ...
Establishment of Prostate Segmentation Model Based on the Convolutional Neural Network Algorithm. ...
doi:10.1155/2021/1034661
pmid:34873435
pmcid:PMC8643240
fatcat:nnt2nkleurhp7beuffjpbtaa5e
Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review
2021
IEEE Access
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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 ...
1242 of Gleason Grade Group in prostate cancer. ...
doi:10.1109/access.2021.3090825
fatcat:l2xe2tdwk5b6ldn7axvzbp5a5a
A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect ...
Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. ...
Fully Convolutional Networks (FCNs) To forecast semantic segmentation, Fully Convolutional Networks is a popular algorithm used to practice end to end pixel-wise prediction [10] .
Fig. 3. ...
doi:10.35940/ijitee.k7654.0991120
fatcat:4qkd3kdqvjgu7n2g7wnmnyiici
Effects of annotation granularity in deep learning models for histopathological images
[article]
2020
arXiv
pre-print
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Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. ...
Intelligence systems trained on granular annotations may help pathologists inspecting certain regions for better diagnosis. ...
The second one is semantic segmentation which can acquire the morphological features of cancer cells on slides. We use full convolutional neural network to do the semantic segmentation experiment. ...
arXiv:2001.04663v1
fatcat:73wdxjujebantijjjmjiptq4xm
Prostate Gland Segmentation in Histology Images via Residual and Multi-resolution U-NET
[chapter]
2020
Lecture Notes in Computer Science
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2021 pre-print arXiv:2105.10556v1 fatcat:z6dz3iacnrao3kdpplxianipya
The methodological basis of this work is a prostate gland segmentation based on U-Net convolutional neural network architectures modified with residual and multi-resolution blocks, trained using data augmentation ...
Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. ...
To the best of the authors' knowledge, the best performing state-of-the-art techniques for semantic segmentation, based on convolutional neural networks, have not been studied yet for prostate gland segmentation ...
doi:10.1007/978-3-030-62362-3_1
fatcat:bodjvgijq5ajbdthldw6inyrre
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
[article]
2019
arXiv
pre-print
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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. ...
increased accuracy and “Cgc-net: Cell graph convolutional network for grading
efficiency of histopathological diagnosis,” Scientific reports, of colorectal cancer histology images,” ...
arXiv:1910.13328v2
fatcat:cxhdf6ipjzahphg2p66mkj3aqe
Front Matter: Volume 10134
2017
Medical Imaging 2017: Computer-Aided Diagnosis
Preserved Fulltext
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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. ...
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 ...
]
10134 29
Detection of prostate cancer on multiparametric MRI [10134-271]
10134 2A
Classification of clinical significance of MRI prostate findings using 3D convolutional neural
networks [10134 ...
doi:10.1117/12.2277119
dblp:conf/micad/X17
fatcat:ika7pheqxngdxejyvkss4dkbv4
Improving Prostate Cancer Detection with Breast Histopathology Images
[article]
2019
arXiv
pre-print
Preserved Fulltext
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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. ...
., InceptionV3 [25] and the study by Isaksson et al. [11] proposes a U-net [20] based semantic segmentation of prostate tissue. ...
arXiv:1903.05769v1
fatcat:flpb7sd2kzfjdozaqj6s47zfaa
Front Matter: Volume 10581
2018
Medical Imaging 2018: Digital Pathology
Preserved Fulltext
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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. ...
The sessions included Emerging Trends; Machine Learning Trends; Diagnosis, Prognosis, and Predictive Analysis; Precision Medicine and Grading; and Detection and Segmentation. ...
Semantic segmentation for prostate cancer grading by convolutional neural networks
[10581-46]
10581 1C
SlideSeg: a Python module for the creation of annotated image repositories from whole slide
images ...
doi:10.1117/12.2323941
fatcat:wyt7wxgl4nebxooizvt2efswwq
Front Matter: Volume 10575
2018
Medical Imaging 2018: Computer-Aided Diagnosis
Preserved Fulltext
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Papers were selected and subject to review by the editors and conference program committee. Some conference presentations may not be available for publication. ...
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
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
Image-based patch selection for deep learning to improve automated Gleason grading in histopathological slides
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
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Automated Gleason grading can be a valuable tool for physicians when assessing risk and planning treatment for prostate cancer patients. ...
This method was used to generate patches for a fully convolutional network to segment high grade, low grade, and benign tissue from a set of 59 histopathological slides, and results were compared against ...
This network is a region-based convolutional neural network (R-CNN) framework for multitask prediction using an Epithelial Network Head and a Grading Network Head. ...
doi:10.1101/2020.09.26.314989
fatcat:5fj2ns77brdyxfw3qmg2d2qm64
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