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Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
[article]
2017
arXiv
pre-print
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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
2021
Translational and Regulatory Sciences
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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
2021
Laboratory Investigation
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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
2019
British Journal of Dermatology
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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
2019
Cancers
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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
2020
Applied Sciences
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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]
2016
arXiv
pre-print
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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
2021
Pathobiology (Basel)
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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
2021
Cancers
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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
2016
Medical Image Analysis
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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]
2021
arXiv
pre-print
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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
2021
Bioinformatics
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2021
Security and Communication Networks
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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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