2 citations · 3 across the 5 of their papers we have counts for
5 papers
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +4
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial du…
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi +3
Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor beh…
FedDropoutAvg: Generalizable federated learning for histopathology image classification
Gozde N. Gunesli, Mohsin Bilal, Shan E Ahmed Raza +1
Federated learning (FL) enables collaborative learning of a deep learning model without sharing the data of participating sites. FL in medical image analysis tasks is relatively ne…
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…
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…