138 citations · 459 across the 15 of their papers we have counts for
17 papers
Deep Learning-based Frozen Section to FFPE Translation
Kutsev Bengisu Ozyoruk, Sermet Can, Guliz Irem Gokceler +12
Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of the procedure allows pathologists to…
VR-Caps: A Virtual Environment for Capsule Endoscopy
Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid +8
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate…
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Gulfize Coskun +12
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not sup…
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce +8
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both…
ModelHub.AI: Dissemination Platform for Deep Learning Models
Ahmed Hosny, Michael Schwier, Christoph Berger +13
Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among…
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation
Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimat…
Milli-RIO: Ego-Motion Estimation with Low-Cost Millimetre-Wave Radar
Yasin Almalioglu, Mehmet Turan, Chris Xiaoxuan Lu +2
Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among…
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Evin Pinar Ornek +3
Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging…
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
Mehmet Turan, Evin Pinar Ornek, Nail Ibrahimli +4
In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into act…
EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Hunter Gilbert +3
A reliable, real time multi-sensor fusion functionality is crucial for localization of actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagn…
Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data
Mehmet Turan, Yasin Almalioglu, Hunter Gilbert +3
In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major c…
Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Helder Araujo +2
Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologi…
Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots
Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu +3
Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be co…
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
Mehmet Turan, Yusuf Yigit Pilavci, Redhwan Jamiruddin +3
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI…
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Helder Araujo +2
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI…
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Ender Konukoglu +1
We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsul…
Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
Mehmet Turan, Yasin Almalioglu, Hunter Gilbert +3
A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emergin…