Publications by authors named "Gulfize Coskun"
Article Synopsis
- Endomyocardial biopsy (EMB) is crucial for detecting heart transplant rejections but suffers from inconsistent manual interpretation by pathologists, leading to unnecessary treatments and poor outcomes.
- A new AI system has been developed to automate the analysis of EMB images, accurately detecting various types and grades of allograft rejection with high performance metrics.
- In comparison to traditional methods, this AI system not only maintains equivalent accuracy but also reduces variability among different human reviewers, potentially enhancing the effectiveness of heart transplant monitoring.
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth.
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