Ali Mahmoud

TL;DR: Wang et al. as mentioned in this paper proposed a 3D-CNN model based on a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain.

TL;DR: The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care.

TL;DR: This paper presents a direct method to shape recovery using both polarization and shading that resolves this ambiguity, without the need for nonlinear optimization routines.

TL;DR: The potential of the proposed computer-aided diagnostic system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol is demonstrated.

TL;DR: A computer-aided diagnosis system that utilizes structural MRI and resting-state functional MRI to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism.