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.
KEYWORDS: Nanoparticles, Iron, Magnetic resonance imaging, Gold, Computed tomography, Particles, In vivo imaging, Transmission electron microscopy, Molecules
Multimodal imaging, integrating different imaging modalities, is emerging as a promising strategy for improving both preclinical and clinical imaging. Computed tomography (CT) and magnetic resonance imaging (MRI) are among the widely clinically-used imaging techniques. These imaging modalities are used for disease diagnosis and screening, each providing different and complementary information about the patient and the disease state. Nanoparticles are considered promising contrast agents for in vivo imaging applications, providing improved imaging and targeting capabilities compared to the commonly used contrast compounds. In this project, we present the development of a novel hybrid nanostructure, comprising of iron oxide core and gold shell nanoparticle for dual model CT and MRI imaging. The hybrid structures presented herein have potential to serve as a unique platform for precision imaging.
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