337 Hits in 4.7 sec

Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net [article]

Amartya Kalapahar, Julio Silva-Rodríguez, Adrián Colomer, Fernando López-Mir, Valery Naranjo
2020 arXiv   pre-print
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

Julio Silva-Rodríguez, Adrián Colomer, Valery Naranjo
2021 Computerized Medical Imaging and Graphics  
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

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.  ...  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

Jiayun Li, Karthik V Sarma, King Chung Ho, Arkadiusz Gertych, Beatrice S Knudsen, Corey W Arnold
2018 AMIA Annual Symposium Proceedings  
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

Weijun Gao, Peibo Zhang, Hui Wang, Pengfei Tuo, Zhiqing Li, Kalidoss Rajakani
2021 Journal of Healthcare Engineering  
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

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  ...  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

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]

Jiangbo Shi, Zeyu Gao, Haichuan Zhang, Pargorn Puttapirat, Chunbao Wang, Xiangrong Zhang, Chen Li
2020 arXiv   pre-print
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]

Julio Silva-Rodríguez, Elena Payá-Bosch, Gabriel García, Adrián Colomer, Valery Naranjo
2020 Lecture Notes in Computer Science  
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]

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.  ...  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  
Publication of record for individual papers is online in the SPIE Digital Library. 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]

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.  ...  ., 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

Metin N. Gurcan, John E. Tomaszewski
2018 Medical Imaging 2018: Digital Pathology  
Publication of record for individual papers is online in the SPIE Digital Library. 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

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
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]

William Speier, Jiayun Li, Wenyuan Li, Karthik Sarma, Corey Arnold
2020 bioRxiv   pre-print
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
« Previous Showing results 1 — 15 out of 337 results