Mr. Chen Solomon Profile

Chen Solomon

at Tel Aviv Univ.

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Proceedings Article | 4 April 2022 Presentation + Paper

Deep learning based multiple sclerosis lesion detection utilizing synthetic data generation and soft attention mechanism

Omer Zucker Shmueli, Chen Solomon, Noam Ben-Eliezer, Hayit Greenspan

Proceedings Volume 12033, 120330R (2022) https://doi.org/10.1117/12.2613283

KEYWORDS: Brain, Neuroimaging, Magnetic resonance imaging, Network architectures, Medical imaging, Visualization, Performance modeling, Nervous system, Machine learning

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In this work we focus on identifying healthy brain slices vs brain slices with Multiple sclerosis (MS) lesions. MS is an autoimmune, demyelinating disease characterized by inflammatory lesions in the central nervous system. MRI is commonly used for diagnosis of MS, and enables accurate detection and classification of lesions for early diagnosis and treatment. Visual attention mechanisms may be beneficial for the detection of MS brain lesions, as they tend to be small. The attention mechanism prevents overfitting of the background when the amount of data is limited. In addition, enough data is necessary for training a successful machine learning algorithms for medical image analysis. Data with insufficient variability leads to poor classification performance. This is problematic in medical imaging where abnormal findings are uncommon and data labeling requires expensive expert’s time. In this work, we suggest a new network architecture, based on Y-net and EfficientNet models, with attention layers to improve the network performance and reduce overfitting. Furthermore, the attention layers allow extraction of lesion locations. In addition, we show an innovative regularization scheme on the attention weight mask to make it focus on the lesions while letting it search in different areas. Finally, we explore an option to add synthetic lesions in the training process. Based on recent work, we generate artificial lesions in healthy brain MRI scans to augment our training data. Our system achieves 91% accuracy in identifying cases that contain lesions (vs. healthy cases) with more than 13% improvement over an equivalent system without the attention and the data added.

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