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Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images [article]

Bruno Korbar, Andrea M. Olofson, Allen P. Miraflor, Katherine M. Nicka, Matthew A. Suriawinata, Lorenzo Torresani, Arief A. Suriawinata, Saeed Hassanpour
2017 arXiv   pre-print
The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image  ...  Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall  ...  We used these annotations as reference standards for training and testing our deep-learning methods for colorectal polyp classification on whole-slide images, as well as for establishing a deep-learning  ... 
arXiv:1703.01550v2 fatcat:nxppmg3rinevflzw7vbuuyrp2m

Advanced deep learning applications in diagnostic pathology

Daisuke KOMURA, Shumpei ISHIKAWA
2021 Translational and Regulatory Sciences  
Key words: diagnostic pathology, deep learning, whole slide image highlights There have been recent advances in digital pathology and image recognition technology using deep neural networks.  ...  This has led to the emergence of digital pathology, a field in which whole slide images are used for histopathological diagnoses.  ...  Fig. 1 . 1 Whole slide images in digital pathology. (A) Slide scanner (Hamamatsu Photonics NanoZoomer S60).  ... 
doi:10.33611/trs.2021-005 fatcat:fmooffre4jeqtmpfn4m2ba4d3y

Artificial intelligence and computational pathology

Miao Cui, David Y. Zhang
2021 Laboratory Investigation  
Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics.  ...  Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception.  ...  These methods play an essential role in the quality control of whole-slide images to standardize the quality of images in computational pathology.  ... 
doi:10.1038/s41374-020-00514-0 pmid:33454724 fatcat:a6oacxoxhbcv5kebsvm7jkkkf4

Recognizing Basal Cell Carcinoma on Smartphone‐Captured Digital Histopathology Images with Deep Neural Network

Y.Q. Jiang, J.H. Xiong, H.Y. Li, X.H. Yang, W.T. Yu, M. Gao, X. Zhao, Y.P. Ma, W. Zhang, Y.F. Guan, H. Gu, J.F. Sun
2019 British Journal of Dermatology  
Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches.  ...  Deep learning approaches have demonstrated promising results on pathological image-related diagnostic tasks.  ...  Background Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches.  ... 
doi:10.1111/bjd.18026 pmid:31017653 fatcat:a42u36tznzawldylgii5tdxk5i

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  
With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure.  ...  An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images.  ...  steps [56, 57] and non-deep-learning-based image segmentation methods [58] .  ... 
doi:10.3390/cancers11111673 pmid:31661863 pmcid:PMC6895901 fatcat:bntqqbilwrbybdhgfd73px5zki

Computer-Aided Bacillus Detection in Whole-Slide Pathological Images Using a Deep Convolutional Neural Network

Chung-Ming Lo, Yu-Hung Wu, Yu-Chuan (Jack) Li, Chieh-Chi Lee
2020 Applied Sciences  
This study proposed a computer-aided detection (CAD) system based on deep learning to automatically detect acid-fast stained mycobacteria.  ...  After randomly selecting 80% of the samples as the training set and the remaining 20% of samples as the testing set, a transfer learning mechanism based on a deep convolutional neural network (DCNN) was  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10124059 fatcat:uxxtpgdjabasretl54ju4yqm24

Deep Learning for Identifying Metastatic Breast Cancer [article]

Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H. Beck
2016 arXiv   pre-print
These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses.  ...  The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel  ...  Discussion Here we present a deep learning-based system for the automated detection of metastatic cancer from whole slide images of sentinel lymph nodes.  ... 
arXiv:1606.05718v1 fatcat:vofxtbu5kzhyhicx65drhtswnu

Digital Technology in Diagnostic Breast Pathology and Immunohistochemistry

Emad A. Rakha, Konstantinos Vougas, Puay Hoon Tan
2021 Pathobiology (Basel)  
However, the recent application of whole slide imaging technology and artificial intelligence (AI)-based tools has attracted a lot of attention.  ...  Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition systems, and digital technology-based production of antibodies and other IHC  ...  Deep learning-based evaluate HER2 status in whole slide images ligence and digital pathology: challenges and image analysis methods for brightfield-ac- using a modified deep learning  ... 
doi:10.1159/000521149 pmid:34969036 fatcat:rx4rloc5vzch5cc2b7tlqcfxkq

PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data

Guoqing Bao, Xiuying Wang, Ran Xu, Christina Loh, Oreoluwa Daniel Adeyinka, Dula Asheka Pieris, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Kerrie L. McDonald, Anna K. Nowak, Richard Banati (+2 others)
2021 Cancers  
We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections.  ...  Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN.  ...  Acknowledgments: Biopsies were provided by the Australian Genomics and Clinical Outcomes of Glioma (AGOG) tissue bank. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers13040617 pmid:33557152 pmcid:PMC7913958 fatcat:gmtvxokw6ndi3ad3syeizhpg2q

Image analysis and machine learning in digital pathology: Challenges and opportunities

Anant Madabhushi, George Lee
2016 Medical Image Analysis  
whole slide images.  ...  We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and  ...  Unsupervised feature approaches such as deep learning based methods are less intuitive and rely on filter responses solicited from large numbers of training exemplars to characterize and model image appearance  ... 
doi:10.1016/j.media.2016.06.037 pmid:27423409 pmcid:PMC5556681 fatcat:hkfeb37xbrbd3j4zu5h5cnga3q

PIMIP: An Open Source Platform for Pathology Information Management and Integration [article]

Jialun Wu, Anyu Mao, Xinrui Bao, Haichuan Zhang, Zeyu Gao, Chunbao Wang, Tieliang Gong, Chen Li
2021 arXiv   pre-print
Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections.  ...  We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples.  ...  Our team also develops a variety of methods for image analysis, such as [15] - [19] . Based on deep learning models, our platform's image analysis is efficient and accurate.  ... 
arXiv:2111.05794v1 fatcat:d6khfoh5l5aapfohhjrncd3k2a

OpenPhi: An interface to access Philips iSyntax whole slide images for computational pathology

Nita Mulliqi, Kimmo Kartasalo, Henrik Olsson, Xiaoyi Ji, Lars Egevad, Martin Eklund, Pekka Ruusuvuori, Olga Vitek
2021 Bioinformatics  
Summary Digital pathology enables applying computational methods, such as deep learning, in pathology for improved diagnostics and prognostics, but lack of interoperability between whole slide image formats  ...  of different scanner vendors is a challenge for algorithm developers.  ...  Acknowledgements We thank Carin Cavalli-Björkman, Edvin Lökk, Tony Ström and Peter Ström for technical assistance with whole slide scanning and Teemu Tolonen for providing sample WSIs for testing.  ... 
doi:10.1093/bioinformatics/btab578 pmid:34358287 pmcid:PMC8570784 fatcat:zv3t3yxfx5h7tljqi4xdespp7m

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning [article]

Jialun Wu, Haichuan Zhang, Zeyu Gao, Xinrui Bao, Tieliang Gong, Chunbao Wang, Chen Li
2021 arXiv   pre-print
A deep convolutional neural network (InceptionV3) was trained on the high-quality annotated dataset of The Cancer Genome Atlas (TCGA) whole-slide histopathological image for accurate tumor area detection  ...  In this work, we proposed a deep learning-based framework for analyzing histopathological images of patients with renal cell carcinoma, which has the potential to achieve pathologist-level accuracy in  ...  Deep learning (DL) is a powerful method for tumor region detection, subtypes and grades classification of the whole-slide images in digital pathology [5] .  ... 
arXiv:2110.13652v1 fatcat:v7ug23nxorhlbkh4wqcohn4ua4

Artificial intelligence technology in oncology: a new technological paradigm [article]

Mario Coccia
2019 arXiv   pre-print
In this context, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior.  ...  Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence.  ...  costs; moreover, poor regions, with these AI The application of Artificial Intelligence (AI) technology with deep learning algorithms to whole-slide pathology images can potentially improve diagnostic  ... 
arXiv:1905.06871v1 fatcat:wtzcracchneghlon4t4vuqqkhu

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

Xin Liao, Qin Huang, Xin Zheng, Jian Su
2021 Security and Communication Networks  
In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion  ...  Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing  ...  Acknowledgments is research was supported by the grants from Key Laboratory Open Foundation of Sichuan Province .  ... 
doi:10.1155/2021/5868501 fatcat:ivcqsiwtsrbjldistejqa5t6dq
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