211 citations · 415 across the 41 of their papers we have counts for
4 papers · 1 filter
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Partoo Vafaeikia, Matthias W. Wagner, Uri Tabori +2
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While th…
Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
Sajith Rajapaksa, Farzad Khalvati
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNN…
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
Saman Motamed, Farzad Khalvati
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image…
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
Saman Motamed, Farzad Khalvati
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. Wit…