Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network

Yoon, Hong-Jun; Lee, Joohyung; Oh, Ji Eun; Kim, Hong Rae; Lee, Seon-Hye; Chang, Hee Jin; Sohn, Dae Kyung

doi:10.1007/s10278-018-0112-9

Cited by 49 publications

(43 citation statements)

References 9 publications

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“…Finally, the training set is fed to the modified VGG model for training; after each epoch, the training and validation sets’ prediction evaluated. Figure 2 in Yoon et al [ 41 ]. Reprinted with permission from the authors [ 41 ].…”

Section: Figurementioning

confidence: 99%

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Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur

Yoon²,

Chong

2020

Cancers

Self Cite

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Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

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Section: Figurementioning

confidence: 99%

“…Figure 2 in Yoon et al [ 41 ]. Reprinted with permission from the authors [ 41 ]. Copyright (2018), Journal of Digital Imaging published by Springer Link.…”

Section: Figurementioning

confidence: 99%

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur

Yoon²,

Chong

2020

Cancers

Self Cite

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“…ML is also being used in imaging and medical diagnosis. Researchers from South Korea used ML to identify tumors in images from patients’ exams with 93% accuracy [31]. Google’s computers which are trained to detect breast cancer with AI and ML techniques achieved 89% accuracy compared to 73% for doctors [32].…”

Section: Artificial Intelligence In Environment and Health Researchmentioning

confidence: 99%

Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things

Bublitz

Oetomo

Sahu

et al. 2019

IJERPH

110

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The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.

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“…Deep ConvNets were applied in several image recognition applications with high accuracy, and this increased its reliability for future research. 10 – 12 Roy et al 10 explored CNNs for hyperspectral image classification, and Hartenstein et al 11 used deep learning to determine prostate cancer positivity from CT imaging. Yoon et al 12 used a CNN for tumor identification in colorectal histology images.…”

Section: Introductionmentioning

confidence: 99%

“… 10 – 12 Roy et al 10 explored CNNs for hyperspectral image classification, and Hartenstein et al 11 used deep learning to determine prostate cancer positivity from CT imaging. Yoon et al 12 used a CNN for tumor identification in colorectal histology images. These and similar studies motivated researchers to investigate whether AI and ConvNets can be used effectively in COVID-19 research, particularly in diagnostic applications.…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

Şekeroğlu¹,

Ozsahin²

2020

SLAS Technology

134

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The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

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Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network

Cited by 49 publications

References 9 publications

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things

Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

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