Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods

Whitney, Heather M.; Li, Hui; Ji, Yu; Liu, Peifang; Giger, Maryellen L.

doi:10.1109/jproc.2019.2950187

Cited by 54 publications

(25 citation statements)

References 34 publications

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“…Antropova et al [ 100 ] found that a CNN-based classifier slightly outperformed a conventional CADx design (AUC = 0.87 vs. 0.86), and a combination of both approaches performed best (AUC = 0.89). Similar conclusions were reached in other studies [ 104 ]. Other studies, on the contrary, found that CNN significantly outperformed traditional radiomics feature extraction [ 90 , 94 ].…”

Section: Perspectives Of Ai and Deep Learning In Breast Mrisupporting

confidence: 93%

“…Comparison of deep learning vs. hand engineered features was performed by several authors [ 90 , 94 , 100 , 104 ]. Antropova et al [ 100 ] found that a CNN-based classifier slightly outperformed a conventional CADx design (AUC = 0.87 vs. 0.86), and a combination of both approaches performed best (AUC = 0.89).…”

Section: Perspectives Of Ai and Deep Learning In Breast Mrimentioning

confidence: 99%

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Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Meyer-Bäse

Morra

Meyer-Bäse

et al. 2020

Contrast Media & Molecular Imaging

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Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

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Section: Perspectives Of Ai and Deep Learning In Breast Mrisupporting

confidence: 93%

Section: Perspectives Of Ai and Deep Learning In Breast Mrimentioning

confidence: 99%

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Meyer-Bäse

Morra

Meyer-Bäse

et al. 2020

Contrast Media & Molecular Imaging

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“…Abdikenov et al [17] proposed a Pareto optimality-based prognostic model to understanding changes in hyper-parameters in various performance metrics, and [18] presented a wrapper method that embeds Bayesian classifiers for hybrid feature selection of breast cancer datasets. Whitney et al [19] used the most popular integrated methods of deep convolutional neural networks (CNNs) and transfer learning to compare breast magnetic resonance Magnetic Resonance Imaging (MRI) tumor classification with traditional methods. The hybrid CNN-SVM model that was proposed in [20] used the MNIST numerical database that contains 70,000 examples for the experiment.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Chiu

Kuo

2020

IEEE Access

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“…Images from Database A had been acquired in the axial plane and images from Database B were acquired in the sagittal plane. The details of the imaging protocols are available elsewhere [ 5 , 13 , 14 , 15 ]. Information regarding subtypes of benign lesions and cancers had been collected from pathology and imaging reports.…”

Section: Methodsmentioning

confidence: 99%

Multi-Stage Harmonization for Robust AI across Breast MR Databases

Whitney

et al. 2021

Cancers

Self Cite

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Radiomic features extracted from medical images may demonstrate a batch effect when cases come from different sources. We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of breast lesions after fuzzy c-means segmentation, was collected. There were 944 unique lesions in Database A (208 benign lesions, 736 cancers) and 1986 unique lesions in Database B (481 benign lesions, 1505 cancers). The lesions from each database were divided by year of image acquisition into training and independent test sets, separately by database and in combination. ComBat batch harmonization was conducted on the combined training set to minimize the batch effect on eligible features by database. The empirical Bayes estimates from the feature harmonization were applied to the eligible features of the combined independent test set. The training sets (A, B, and combined) were then used in training linear discriminant analysis classifiers after stepwise feature selection. The classifiers were then run on the A, B, and combined independent test sets. Classification performance was compared using pre-harmonization features to post-harmonization features, including their corresponding feature selection, evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Four out of five training and independent test scenarios demonstrated statistically equivalent classification performance when compared pre- and post-harmonization. These results demonstrate that translation of machine learning techniques with batch data harmonization can potentially yield generalizable models that maintain classification performance.

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Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods

Cited by 54 publications

References 34 publications

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

Multi-Stage Harmonization for Robust AI across Breast MR Databases

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