Beyond human-level accuracy

Hestness, Joel; Ardalani, Newsha; Diamos, Gregory

doi:10.1145/3293883.3295710

Cited by 67 publications

(38 citation statements)

References 9 publications

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“…Previous research has demonstrated that increasing the depth of the CNN may improve super-resolution quality (29). Moreover, deep-learning models have empirically shown to follow a power-law loss relationship, which may help determining ideal training data size (52).…”

Section: Discussionmentioning

confidence: 99%

Super‐resolution musculoskeletal MRI using deep learning

Chaudhari

Fang²,

Kogan

et al. 2018

Magnetic Resonance in Med

317

205

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Section: Discussionmentioning

confidence: 99%

Super‐resolution musculoskeletal MRI using deep learning

Chaudhari

Fang²,

Kogan

et al. 2018

Magnetic Resonance in Med

317

205

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“…Some limitations of the study are discussed below. Deep learning algorithms have been shown to scale well and benefit from training on large datasets [27,28]. However, manually annotating large amounts of data for training is a very time consuming and expensive process.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs

Chada

2019

Reports

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Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation.

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“…In short, deep learning deals with, and leverages, vast amounts of information whereas traditional machine-learning methods require human intervention to reduce the size of data using various feature reduction and feature selection techniques ( Mwangi et al , 2014 ; Hestness et al , 2017 ). An intuitive way to appreciate how deep-learning works comes from understanding the firing patterns of a neuron in the brain ( Savage, 2019 ).…”

Section: Deep Learning To Extract High-level Information From Large Amentioning

confidence: 99%

Artificial intelligence for clinical decision support in neurology

Pedersen

Verspoor

Jenkinson

et al. 2020

Brain Communications

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Abstract Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this paper, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasise the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. Given that artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.

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Beyond human-level accuracy

Cited by 67 publications

References 9 publications

Super‐resolution musculoskeletal MRI using deep learning

Super‐resolution musculoskeletal MRI using deep learning

Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs

Artificial intelligence for clinical decision support in neurology

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