831 citations · 852 across the 11 of their papers we have counts for
12 papers
Visible and Infrared Image Fusion Using Encoder-Decoder Network
Ferhat Can Ataman, Gözde Bozdaği Akar
The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we…
Binarized Weight Error Networks With a Transition Regularization Term
Savas Ozkan, Gozde Bozdagi Akar
This paper proposes a novel binarized weight network (BT) for a resource-efficient neural structure. The proposed model estimates a binary representation of weights by taking into…
Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss
Savas Ozkan, Gozde Bozdagi Akar
This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they ar…
Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging
Bora Baydar, Savas Ozkan, A. Emre Kavur +3
Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detaile…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network
Bora Baydar, Savas Ozkan, Gozde Bozdagi Akar
Automatic segmentation of medical images is among most demanded works in the medical information field since it saves time of the experts in the field and avoids human error factor…