3 papers
eess.IV2023
Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images
Seher Ozcelik, Sinan Unver, Ilke Ali Gurses +2
Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are tra…
cs.CV2019
DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images
Can Fahrettin Koyuncu, Gozde Nur Gunesli, Rengul Cetin-Atalay +1
This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as…
cs.CV2019
AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks
Gozde Nur Gunesli, Cenk Sokmensuer, Cigdem Gunduz-Demir
Dense prediction models are widely used for image segmentation. One important challenge is to sufficiently train these models to yield good generalizations for hard-to-learn pixels…