216 citations · 816 across the 46 of their papers we have counts for
8 papers · 1 filter
Circular-Symmetric Correlation Layer based on FFT
Bahar Azari, Deniz Erdogmus
Despite the vast success of standard planar convolutional neural networks, they are not the most efficient choice for analyzing signals that lie on an arbitrarily curved manifold,…
From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels
Mo Han, Sezen Ya{ğ}mur Günay, İlkay Yıldız +5
Limb deficiency severely affects the daily lives of amputees and drives efforts to provide functional robotic prosthetic hands to compensate this deprivation. Convolutional neural…
Deep feature transfer between localization and segmentation tasks
Szu-Yeu Hu, Andrew Beers, Ken Chang +8
In this paper, we propose a new pre-training scheme for U-net based image segmentation. We first train the encoding arm as a localization network to predict the center of the targe…
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus +3
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training su…
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus +1
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based m…
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Seyed Sadegh Mohseni Salehi, Seyed Raein Hashemi, Clemente Velasco-Annis +5
Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of th…
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour
Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbal…
Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is…