activity
20172021
most citedDeep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

138 citations · 309 across the 10 of their papers we have counts for

collaborators

17 papers

eess.IV2021

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…

cs.CV2020

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…

cs.CV20205 cited

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…

cs.CV2020

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…

cs.LG20199 cited

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…

cs.CV2019

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…