Recurrent neural networks for breast lesion classification based on DCE-MRIs
| Autor: | Benjamin Q. Huynh, Natasha Antropova, Maryellen L. Giger |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Deep learning Magnetic resonance imaging Pattern recognition 02 engineering and technology medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Recurrent neural network Breast cancer Dynamic contrast-enhanced MRI 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business Cancer staging |
| Zdroj: | Medical Imaging: Computer-Aided Diagnosis |
| Popis: | Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03). |
| Databáze: | OpenAIRE |
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