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Deep Learning for Prostate Pathology [article]

Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmood Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas Loening, Robert West, Geoffrey Sonn (+4 others)
2019 arXiv   pre-print
The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain histology images captured  ...  Our models cover the major components of prostate pathology and successfully accomplish the annotation tasks.  ...  Acknowledgment PlexusNet and the derivate digital markers for survival and genomic alteration are patented.  ... 
arXiv:1910.04918v3 fatcat:svjtmndhdzdshhuxi32dqnccqe

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  
This paper presents an automated Gleason grading and Gleason pattern region segmentation method based on deep learning for pathological images of prostate cancer.  ...  INDEX TERMS Prostate cancer, gleason grading, image segmentation, deep learning, atrous spatial pyramid pooling, computer-aided diagnosis.  ...  Certainly, there are many applications of deep learning in the Gleason grading of pathological images of prostate cancer. Ing et al.  ... 
doi:10.1109/access.2020.3005180 fatcat:pghwqk26nzgshm3uwz2y5mxkwm

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

Jun Akatsuka, Yoichiro Yamamoto, Tetsuro Sekine, Yasushi Numata, Hiromu Morikawa, Kotaro Tsutsumi, Masato Yanagi, Yuki Endo, Hayato Takeda, Tatsuro Hayashi, Masao Ueki, Gen Tamiya (+6 others)
2019 Biomolecules  
Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning.  ...  of pathological images.  ...  Acknowledgments: We are grateful to the staffs at radiology department and pathology department of NMSH. Conflicts of Interest: The authors declare no potential conflict of interest.  ... 
doi:10.3390/biom9110673 pmid:31671711 pmcid:PMC6920905 fatcat:oocvvlypu5gitcxbblnwrri3ym

AI reality check when evaluating difficult to grade prostate cancers

Liron Pantanowitz, Rohit Mehra, L. Priya Kunju
2021 Virchows Archiv  
Such digital datasets have, in turn, accelerated the development of machine and deep learning algorithms in anatomical pathology.  ...  Furthermore, it appears that other investigators employing similar deep learning algorithms to grade prostate adenocarcinoma have experienced equal difficulty achieving All authors contributed equally  ...  Such digital datasets have, in turn, accelerated the development of machine and deep learning algorithms in anatomical pathology.  ... 
doi:10.1007/s00428-021-03045-1 pmid:33528621 fatcat:x532l4irufdzncxmgimvtq7ckq

Current status of deep learning applications in abdominal ultrasonography

Kyoung Doo Song
2020 Ultrasonography  
Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted  ...  Deep learning is one of the most popular artificial intelligence techniques used in the medical field.  ...  Prostate Most studies applying deep learning to prostate US imaging have focused on detecting and grading prostate cancer and the Other Abdominal Organs There are no reports of deep learning being applied  ... 
doi:10.14366/usg.20085 pmid:33242931 pmcid:PMC7994733 fatcat:bryjbuxtojdyhfiuwsqskk5idm

The assessment of deep learning computer vision algorithms for the diagnosis of prostatic adenocarcinoma

2021 The Annals of Clinical and Analytical Medicine  
The application of deep learning for the histological diagnosis of malignant tumors could be quite a helpful tool for better patient care.  ...  Aim: In this study, we aimed to evaluate the effectiveness of artificial intelligence for the histopathological diagnosis of prostatic adenocarcinoma by analyzing the digitized pathology slides.  ...  Hassan Bokhari and Faran Mazhar for their valuable assistance.  ... 
doi:10.4328/acam.20322 fatcat:6in6uj4cordalaqgl3qjnvyeyy

The assessment of Computer Vision Algorithms for the Diagnosis of Prostatic Adenocarcinoma in Surgical Specimens [article]

Syed Usama Khalid Bukhari, Ubeer Mehtab, Syed Shahzad Hussain, Asmara Syed, Syed Umar Armaghan, Syed Sajid Hussain Shah
2020 medRxiv   pre-print
The application of deep learning for the histological diagnosis of malignant tumors could be quite helpful in improving the patient care.  ...  In radiology, the application of deep learning to interpret radiological images has revealed excellent results.  ...  Acknowledgement: The authors are grateful to Farhan Mazher for his valuable assistance. Funding: None  ... 
doi:10.1101/2020.07.14.20152116 fatcat:nkgwzuyyyzgsxepvnghzsjkuxi

Bridging the gap between prostate radiology and pathology through machine learning [article]

Indrani Bhattacharya, David S. Lim, Han Lin Aung, Xingchen Liu, Arun Seetharaman, Christian A. Kunder, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Pejman Ghanouni, Katherine J. To'o, James D. Brooks (+2 others)
2021 arXiv   pre-print
pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI.  ...  Prostate cancer is the second deadliest cancer for American men.  ...  Prior machine learning methods for prostate cancer detection include traditional machine learning 9, 10, 11, 12 as well as deep learning models using MRI 13, 14, 15, 16, 17, 18 .  ... 
arXiv:2112.02164v1 fatcat:qdrpiaxhdfg2vdczc2zfofpxqa

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
Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation.  ...  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.  ...  grading of gliomas using deep learning in digital pathology neural networks,” in 2017 IEEE 14th International Symposium images: A modular approach with ensemble of convolutional  ... 
arXiv:1910.13328v2 fatcat:cxhdf6ipjzahphg2p66mkj3aqe

Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

Ciprian Cosmin Secasan, Darian Onchis, Razvan Bardan, Alin Cumpanas, Dorin Novacescu, Corina Botoca, Alis Dema, Ioan Sporea
2022 Current Oncology  
exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate  ...  (1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological  ...  In urology, deep learning algorithms were successfully used for the detection of prostate cancer, correlating mp-MRI images with the results of prostate biopsy, and for the outcome prediction after the  ... 
doi:10.3390/curroncol29060336 pmid:35735445 pmcid:PMC9221963 fatcat:3qhheeyzxnct3a42znhi2zuqie

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
Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images.  ...  In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance.  ...  In addition, we show that this approach outperforms models pre-trained on ImageNet dataset which has been the standard dataset for transfer learning models in deep learning-based digital pathology analysis  ... 
arXiv:1903.05769v1 fatcat:flpb7sd2kzfjdozaqj6s47zfaa

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

Jae Kwon Kim, Mun Joo Choi, Jong Sik Lee, Jun Hyuk Hong, Choung-Soo Kim, Seong Il Seo, Chang Wook Jeong, Seok-Soo Byun, Kyo Chul Koo, Byung Ha Chung, Yong Hyun Park, Ji Youl Lee (+1 others)
2018 Journal of Healthcare Engineering  
We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer.  ...  The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs.  ...  Deep belief networks (DBN) are a deep learning technique and is an effective method for classification prediction [13, 14] .  ... 
doi:10.1155/2018/4651582 pmid:29755715 pmcid:PMC5884161 fatcat:ki57eidiuvbozf327dz4kj3eau

660 Developing generalizable deep learning models for tumor segmentation in pathology images to enable the identification of predictive biomarkers for immunotherapies

Qinle Ba, Peng Yang, Jennifer Yearley
2020 Journal for ImmunoTherapy of Cancer  
This research will help build deep learning models that significantly reduce the need for expert manual annotations.MethodsAnnotated colorectal cancer (CRC)1 (target domain) and prostate cancer (source  ...  Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard. Sci Rep 2019;9, 864.  ...  Machine learning, artificial intelligence, and computational modeling DEVELOPING GENERALIZABLE DEEP LEARNING MODELS FOR TUMOR SEGMENTATION IN PATHOLOGY IMAGES TO ENABLE THE IDENTIFICATION OF PREDICTIVE  ... 
doi:10.1136/jitc-2020-sitc2020.0660 fatcat:wydxcg2fkjephl3p67bphp7c4y

Role of artificial intelligence in integrated analysis of multi-omics and imaging data in cancer research

Nam Nhut Phan, Amrita Chattopadhyay, Eric Y. Chuang
2019 Translational Cancer Research  
Acknowledgments We thank Melissa Stauffer, PhD, for editing the manuscript. Footnote  ...  Deep learning has also been used for tissue origin classification, nuclear grading, precision medicine matching trials (1, 19, 20) , classification of ancient and modern DNA (21) , and drug repurposing  ...  In recent years, machine learning and deep learning-based approaches, two sub-fields of artificial intelligence, have emerged as key components in biomedical data analyses (1) (2) (3) (4) (5) .  ... 
doi:10.21037/tcr.2019.12.17 pmid:35117055 pmcid:PMC8797959 fatcat:p62kxyzqm5ez3mdh76xzcs52ua

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  
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  ...  In conclusion, MRI image segmentation algorithm based on CNN was more accurate in the diagnosis and risk stratification of prostate cancer than routine MRI.  ...  [15] proposed for the first time and perfectly combined fully CNN (FCNN) with image segmentation to create a new path for image segmentation. With the advancement of deep learning, Li et al.  ... 
doi:10.1155/2021/1034661 pmid:34873435 pmcid:PMC8643240 fatcat:nnt2nkleurhp7beuffjpbtaa5e
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