Y. Senli

The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas.

N. Kocer, D. Alis, M. Yergin, 2020, Clinical radiology.

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

D. Alis, M. Yergin, O. Asmakutlu, 2020, Scientific Reports.

Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas

N. Kocer, D. Alis, M. Yergin, 2019, Japanese Journal of Radiology.

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT

D. Alis, M. Yergin, O. Asmakutlu, 2022, Scientific Reports.