2 citations · 2 across the 3 of their papers we have counts for
9 papers
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…
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty
Savas Ozkan, Gozde Bozdagi Akar
In this study, we propose a novel framework for hyperspectral unmixing by using an improved deep spectral convolution network (DSCN++) combined with endmember uncertainty. DSCN++ i…
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search
Savas Ozkan, Gozde Bozdagi Akar
The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardw…