Deep learning in biomedicine

Nature Biotechnology, Год журнала: 2018, Номер 36(9), С. 829 - 838

Опубликована: Сен. 6, 2018

Язык: Английский

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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis

Medical Image Analysis, Год журнала: 2020, Номер 65, С. 101759 - 101759

Опубликована: Июнь 20, 2020

Язык: Английский

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The importance of interpretability and visualization in machine learning for applications in medicine and health care

Neural Computing and Applications, Год журнала: 2019, Номер 32(24), С. 18069 - 18083

Опубликована: Фев. 4, 2019

Язык: Английский

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Strategies for Pre-training Graph Neural Networks

arXiv (Cornell University), Год журнала: 2019, Номер unknown

Опубликована: Янв. 1, 2019

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels scarce during training. An effective approach this challenge is pre-train related tasks where data abundant, and then fine-tune it downstream task interest. While pre-training has been in many language vision domains, remains an open question how effectively use graph datasets. In paper, we develop new strategy self-supervised methods for Graph Neural Networks (GNNs). The key the success our expressive GNN at level individual nodes as well entire graphs so can learn useful local global representations simultaneously. We systematically study multiple classification find naive strategies, which GNNs either or nodes, give limited improvement even lead negative transfer tasks. contrast, avoids improves generalization significantly across tasks, leading up 9.4% absolute improvements ROC-AUC over non-pre-trained models achieving state-of-the-art performance molecular property prediction protein function prediction.

Язык: Английский

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Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis

The Lancet Digital Health, Год журнала: 2021, Номер 3(3), С. e195 - e203

Опубликована: Янв. 19, 2021

There has been a surge of interest in artificial intelligence and machine learning (AI/ML)-based medical devices. However, it is poorly understood how which AI/ML-based devices have approved the USA Europe. We searched governmental non-governmental databases to identify 222 240 The number increased substantially since 2015, with many being for use radiology. few were qualified as high-risk Of 124 commonly Europe, 80 first One possible reason approval Europe before might be potentially relatively less rigorous evaluation substantial highlight need ensure regulation these Currently, there no specific regulatory pathway or recommend more transparency on are regulated enable improve public trust, efficacy, safety, quality A comprehensive, publicly accessible database device details Conformité Européene (CE)-marked US Food Drug Administration needed.

Язык: Английский

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