514 citations · 547 across the 6 of their papers we have counts for
6 papers
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
Ecem Sogancioglu, Keelin Murphy, Ernst Th. Scholten +3
Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-…
Deep Learning for Chest X-ray Analysis: A Survey
Ecem Sogancioglu, Erdi Çallı, Bram van Ginneken +2
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a…
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
Erdi Çallı, Keelin Murphy, Ecem Sogancioglu +1
In this work, we propose a method to reject out-of-distribution samples which can be adapted to any network architecture and requires no additional training data. Publicly availabl…
Context Encoding Chest X-rays
Davide Belli, Shi Hu, Ecem Sogancioglu +1
Chest X-rays are one of the most commonly used technologies for medical diagnosis. Many deep learning models have been proposed to improve and automate the abnormality detection ta…
Chest X-ray Inpainting with Deep Generative Models
Ecem Sogancioglu, Shi Hu, Davide Belli +1
Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the…