Multi-modal deep-fusion network for meningioma presurgical grading with integrative imaging and clinical data

Author:

Liu WennanORCID,Liu Tianling,Han Tong,Wan Liang

Funder

Natural Science Foundation of Tianjin City

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Reference34 articles.

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2. Banerjee, S., Mitra, S., Masulli, F., Rovetta, S.: Deep radiomics for brain tumor detection and classification from multi-sequence mri. arXiv preprint arXiv:1903.09240 (2019)

3. Banerjee, S., Mitra, S., Masulli, F., Rovetta, S.: Deep radiomics for brain tumor detection and classification from multi-sequence mri. arXiv preprint arXiv:1903.09240 (2019)

4. Cornelius, J.F., Slotty, P.J., Steiger, H.J., Hänggi, D., Polivka, M., George, B.: Malignant potential of skull base versus non-skull base meningiomas: clinical series of 1,663 cases. Acta Neurochirurgica 155(3), 407–413 (2013)

5. Dai, Z., Liu, H., Le, Q.V., Tan, M.: Coatnet: marrying convolution and attention for all data sizes. Adv. Neural Inf. Process. Syst. 34, 3965–3977 (2021)

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