Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

Zhou, Hao; Hu, Rong; Tang, Oliver Y.; Hu, Chongyu; Tang, Lu; Chang, Ken; Shen, Qin; Wu, Jie; Zou, Beiji; Xiao, Bing; Boxerman, Jerrold L.; Chen, W.; Huang, Raymond Y.; Yang, Lili; Bai, Harrison X.; Zhu, Chengzhang

doi:10.3174/ajnr.a6621

Cited by 50 publications

(52 citation statements)

References 31 publications

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“…Several studies have shown the feasibility of PFT classification using conventional machine learning and MRI data. A few studies used histogram textural analysis and visual based features extracted from diffusion and conventional MRI, and patients' clinical features [6,[16][17][18][19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

et al. 2021

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Posterior fossa tumors (PFT) are the most common tumors in children. Differentiation between the various PFT types is critical, as different tumors have diverse treatment approaches. This study proposes the use of fused architecture comprising two neural networks, a pre-trained ResNet-50 Convolutional Neural Network (CNN) and a tabular based network for the classification of PFT. The study included data for 158 MRI scans of 22 healthy controls and 136 pediatric patients with newly diagnosed PFT (63 Pilocytic Astrocytoma, 57 Medulloblastoma and 16 Ependymoma). The input data for classification were from magnetic resonance imaging: post contrast T1-weighted, fluid attenuated inversion recovery and diffusion Trace images, and tabular data: subject's age. Evaluation of the model was performed in a stratified 5-fold cross-validation manner, based on accuracy, precision, recall and F1 score metrics. Model explanation was performed in terms of visual explanation of the CNN by Gradient-weighted Class Activation Mapping (Grad-CAM) and by testing the contribution to the classification results of the different imaging input data sets and the proposed fused architectures relative to CNN only and tabular only architectures. The best classification results were obtained with the fused CNN + tabular data architecture, and based on diffusion Trace images, achieving mean cross-validation accuracy of 0.88±0.04 for the validation and 0.87±0.02 for the test dataset. Overall, the proposed architecture achieved improvement in accuracy and F1 score compared to CNN method for this dataset.

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Section: Related Workmentioning

confidence: 99%

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

et al. 2021

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“…To enhance the scalability and interpretability, a template for the searched pipeline can be specified, and a selector of the feature set can be appended as the input preprocessing port for every pipeline to separate the model training data by different small feature sets, thus allowing genetic programming to seek the best performance pipeline [ 59 ]. TPOT has been successfully applied in biomedicine [ 59 , 60 ].…”

Section: Methodsmentioning

confidence: 99%

Optimization of running-in surface morphology parameters based on the AutoML model

Liu

Zhang

2021

PLoS ONE

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Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing, and the surface morphology optimization is explored. When better oil storage capacity is required, the recommendation is to increase the Sq, Sdq and Sk surface parameter values while setting medium Sdc and Sdr surface parameter values. When a lower coefficient of friction (COF) is required, Sdc and Sdr should be decreased, and Sq and Sdq should be increased. When better support performance is required, Sdc, Sdq, and Sdr should be increased. This article provides a solution to establish a link between surface design and functional performance in the steady wear stage, further filling the gap in quality monitoring of lifecycles.

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“…In [106], an AutoML model was proposed to do three-way and binary classification of the main types of pediatric posterior fossa tumors based on routine MRI prior to an operation. Here, contrast-enhanced T1-weighted images, T2-weighted images, and ADC maps from histologically confirmed 111 MB, 70 EP, and 107 PA fossa tumor patients are utilized in order to extract radiomics features.…”

Section: ) Ml-based Approaches In Brain Tumor Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

Cited by 50 publications

References 31 publications

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data

Optimization of running-in surface morphology parameters based on the AutoML model

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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