7 April 2023Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors
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Schwannomas and meningiomas account for large proportion of primary spinal tumors and need surgical procedures. Although preoperative discrimination of schwannomas and meningiomas is crucial, differentiation between the two is challenging based on magnetic resonance imaging. The two have not only different patterns of magnetic resonance imaging but also different types of epidemiology. TabNet was recently invented as a deep neural network for tabular data and achieved state-of-the-art results on several datasets. As TabNet is a deep neural network, we can simultaneously train TabNet and a convolutional neural network, allowing simultaneous image and tabular data analysis. We aim to build a bi-modal model combining a convolutional neural network and TabNet and evaluate its performance for differentiating between schwannomas and meningiomas based upon integrated magnetic resonance imaging and clinical factors.
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