Highly accurate model for prediction of lung nodule malignancy with CT scans

Causey, Jason; Zhang, Junyu; Ma, Shiqian; Jiang, Bo; Qualls, Jake; Politte, David G.; Prior, Fred; Zhang, Shuzhong; Huang, Xiuzhen

doi:10.1038/s41598-018-27569-w

Cited by 159 publications

(105 citation statements)

References 35 publications

(42 reference statements)

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“…In recent years, deep learning has propelled the state of the art in segmentation in medical imaging [11,10,6,22,16]. However, previous works tend to focus on maximizing accuracy, ignoring predictive uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

Shi

Worrall

Knegt

et al. 2019

Lecture Notes in Computer Science

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The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity.

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Section: Introductionmentioning

confidence: 99%

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

Shi

Worrall

Knegt

et al. 2019

Lecture Notes in Computer Science

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“…In [18] deep features are extracted from an autoencoder. In [8] high malignancy classification accuracy is achieved by using a convolutional neural network and radiological quantitative features.…”

Section: Related Workmentioning

confidence: 99%

“…An alternative recent solution to this problem is the use of deep convolutional neural networks (e.g. [8,9]) that are able to learn automatically inherent representations directly from the raw images.…”

Section: Introductionmentioning

confidence: 99%

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline

Bonavita

Rafael-Palou

Ceresa

et al. 2020

Computer Methods and Programs in Biomedicine

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Background and Objective: The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and a less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; Methods: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; Results: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7 %, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8 % of F1-weighted score;Conclusions: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.

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“…The CheXNet [14] model was able to accurately identify 14 categories of abnormalities in chest X-ray images. Deep learning techniques have shown promise for automated detection and diagnosis of lung cancer [19][20][21][22], breast cancer [23,24], skin cancer [25][26][27], and other diseases. Most of these approaches use deep neural networks [28] especially convolutional neural networks [29,30].…”

Section: Introductionmentioning

confidence: 99%

Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

Stubblefield

Hervert

Causey

et al. 2020

Preprint

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One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deeplearning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

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Highly accurate model for prediction of lung nodule malignancy with CT scans

Cited by 159 publications

References 35 publications

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline

Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

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