Gastric cancer

The Lancet, Journal Year: 2020, Volume and Issue: 396(10251), P. 635 - 648

Published: Aug. 1, 2020

Language: Английский

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AI in health and medicine

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(1), P. 31 - 38

Published: Jan. 1, 2022

Language: Английский

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Data-efficient and weakly supervised computational pathology on whole-slide images

Nature Biomedical Engineering, Journal Year: 2021, Volume and Issue: 5(6), P. 555 - 570

Published: March 1, 2021

Language: Английский

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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Proceedings of the IEEE, Journal Year: 2021, Volume and Issue: 109(5), P. 820 - 838

Published: Feb. 26, 2021

Since its renaissance, deep learning has been widely used in various medical imaging tasks and achieved remarkable success many applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that of AI mostly attributed to availability big data with annotations for a single task advances high performance computing. However, presents unique challenges confront approaches. In this survey paper, we first present traits imaging, highlight both clinical needs technical describe how emerging trends are addressing these issues. We cover topics network architecture, sparse noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, several case studies commonly found practice, including digital pathology chest, brain, cardiovascular, abdominal imaging. Rather than presenting an exhaustive literature survey, instead some prominent research highlights related study applications. conclude discussion presentation promising future directions.

Language: Английский

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2022, Volume and Issue: 14(7), P. 8459 - 8486

Published: Jan. 13, 2022

Language: Английский

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Deep learning in histopathology: the path to the clinic

Nature Medicine, Journal Year: 2021, Volume and Issue: 27(5), P. 775 - 784

Published: May 1, 2021

Language: Английский

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Deep learning in cancer diagnosis, prognosis and treatment selection

Genome Medicine, Journal Year: 2021, Volume and Issue: 13(1)

Published: Sept. 27, 2021

Abstract Deep learning is a subdiscipline of artificial intelligence that uses machine technique called neural networks to extract patterns and make predictions from large data sets. The increasing adoption deep across healthcare domains together with the availability highly characterised cancer datasets has accelerated research into utility in analysis complex biology cancer. While early results are promising, this rapidly evolving field new knowledge emerging both learning. In review, we provide an overview techniques how they being applied oncology. We focus on applications for omics types, including genomic, methylation transcriptomic data, as well histopathology-based genomic inference, perspectives different types can be integrated develop decision support tools. specific examples may diagnosis, prognosis treatment management. also assess current limitations challenges application precision oncology, lack phenotypically rich need more explainable models. Finally, conclude discussion obstacles overcome enable future clinical utilisation

Language: Английский

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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Nature Cancer, Journal Year: 2020, Volume and Issue: 1(8), P. 800 - 810

Published: July 27, 2020

Language: Английский

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Pan-cancer image-based detection of clinically actionable genetic alterations

Nature Cancer, Journal Year: 2020, Volume and Issue: 1(8), P. 789 - 799

Published: July 27, 2020

Language: Английский

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Deep learning in cancer pathology: a new generation of clinical biomarkers

British Journal of Cancer, Journal Year: 2020, Volume and Issue: 124(4), P. 686 - 696

Published: Nov. 17, 2020

Abstract Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase cost time for decision-making routine daily practice; furthermore, often require tumour tissue top diagnostic material. Nevertheless, routinely available contains an abundance clinically relevant information that is currently not fully exploited. Advances deep learning (DL), artificial intelligence (AI) technology, have enabled extraction previously hidden directly from histology images cancer, providing potentially useful information. Here, we outline emerging concepts how DL can extract summarise studies basic advanced image analysis cancer histology. Basic tasks include detection, grading subtyping images; they are aimed at automating pathology consequently do immediately translate into clinical decisions. Exceeding such approaches, has also been used tasks, which potential affecting processes. These approaches inference features, prediction survival end-to-end therapy response. Predictions made by systems could simplify enrich decision-making, but rigorous external validation settings.

Language: Английский

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