Erratum: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

Esteva, Andre; Kuprel, Brett; Novoa, Roberto A.; Ko, Justin; Swetter, Susan M.; Blau, Helen M.; Thrun, Sebastian

doi:10.1038/nature22985

Cited by 191 publications

(106 citation statements)

References 1 publication

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“…All computations were carried out on F I G U R E 4 Boxplots of intravoxel incoherent motion parameters (D t , F p , and D p ) fitted in upper abdominal organs by the considered algorithms (LS, leastsquares; BP, Bayesian; DNN, deep neural network) based on images acquired at 3.0T. The central marks are the medians, and the boxes extend from the first (Q 1 ) to the third (Q 3 ) data quartile; data points further away than 1.5 times the distance Q 3…”

Section: Average Fitting Timementioning

confidence: 99%

“…In recent years, there has been a renewed interest in the use of artificial neural networks for data classification and regression analysis. Examples of applications in the medical domain include the prognosis of Alzheimer's disease and mild cognitive impairment, classification of digital images of skin lesions with accuracy comparable to human skin‐care specialists, and prediction of patient longevity based on routinely acquired computerized tomography images . Nonetheless, it remains to be seen whether the identification of strong, but theory‐free, associations in clinical data can actually translate into improved clinical care …”

Section: Introductionmentioning

confidence: 99%

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Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI

Barbieri

Gurney‐Champion

Klaassen

et al. 2019

Magnetic Resonance in Med

100

136

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Purpose This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion‐weighted MRI (DW‐MRI) data and evaluates its performance. Methods In May 2011, 10 male volunteers (age range, 29–53 years; mean, 37) underwent DW‐MRI of the upper abdomen on 1.5T and 3.0T MR scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by 2 readers. DNNs were trained for IVIM model fitting using these data; results were compared to least‐squares and Bayesian approaches to IVIM fitting. Intraclass correlation coefficients (ICCs) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using coefficients of variation (CVs). The fitting error was calculated based on simulated data, and the average fitting time of each method was recorded. Results DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the 2 readers (ICCs between 50% and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least‐squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods, but the networks may need to be retrained for different acquisition protocols or imaged anatomical regions. Conclusion DNNs are recommended for accurate and robust IVIM model fitting to DW‐MRI data. Suitable software is available for download.

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Section: Average Fitting Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI

Barbieri

Gurney‐Champion

Klaassen

et al. 2019

Magnetic Resonance in Med

100

136

View full text Add to dashboard Cite

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“…Powered by large labeled datasets [5] and modern GPUs, AI, especially deep learning technique [6], has achieved excellent performance in several computer vision tasks such as image classification [7] and object detection [8]. Recent research shows that AI algorithms can even achieve or exceed the performance of human experts in certain medical image diagnosis tasks [9][10][11][12][13]. The AI diagnosis algorithm also has the advantages of high efficiency, high repeatability and easy large-scale deployment.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of an AI System for COVID-19 Diagnosis

Cheng

Chen

Cao

et al. 2020

Preprint

152

125

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Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https : //github.com

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“…While such molecular tests are now reaching maturity in terms of automation, accuracy and reproducibility, morphological tissue assessment remains a laborious task carried out by trained histopathologists 7,8 . Computer vision and, in particular, deep convolutional neural networks (CNNs) have been shown to closely match the diagnostic accuracy of specialists and thus hold great promise to augment histopathology workflows [9][10][11] . Computational histopathology algorithms can process and cross-reference very large volumes of data, which may help pathologists to navigate and assess slides more quickly and help quantify aberrant cells and tissues.…”

Section: Introductionmentioning

confidence: 99%

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

Jung

Torné

et al. 2019

Preprint

109

166

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Key points• Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types • Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations • Wide-spread correlations with gene expression indicative of immune infiltration and proliferation • Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers AbstractHere we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

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Erratum: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

Cited by 191 publications

References 1 publication

Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI

Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI

Development and Evaluation of an AI System for COVID-19 Diagnosis

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

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