Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors

Kosuke Kita; Takahito Fujimori M.D.; Yuki Suzuki; Seiji Okada M.D.; Shoji Kido M.D.

doi:10.1117/12.2646940

7 April 2023 Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors

Kosuke Kita, Takahito Fujimori M.D., Yuki Suzuki, Seiji Okada M.D., Shoji Kido M.D.

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246506 (2023) https://doi.org/10.1117/12.2646940
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Kosuke Kita, Takahito Fujimori M.D., Yuki Suzuki, Seiji Okada M.D., and Shoji Kido M.D. "Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246506 (7 April 2023); https://doi.org/10.1117/12.2646940

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