Robert Mendel
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Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss Open
Acquiring and annotating large datasets in ultrasound imaging is challenging due to low contrast, high noise, and susceptibility to artefacts. This process requires significant time and clinical expertise. Self-supervised learning (SSL) of…
View article: Artificial Intelligence‐assisted Endoscopy and Examiner Confidence: A Study on Human–Artificial Intelligence Interaction in Barrett's Esophagus (With Video)
Artificial Intelligence‐assisted Endoscopy and Examiner Confidence: A Study on Human–Artificial Intelligence Interaction in Barrett's Esophagus (With Video) Open
Objective Despite high stand‐alone performance, studies demonstrate that artificial intelligence (AI)‐supported endoscopic diagnostics often fall short in clinical applications due to human‐AI interaction factors. This video‐based trial on…
View article: Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial
Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial Open
Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE). Methods 96 s…
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation Open
Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. E…
View article: Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation
Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation Open
Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work…
View article: Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm
Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm Open
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopi…
View article: Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence
Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence Open
The early diagnosis of cancer in Barrett's esophagus is crucial for improving the prognosis. However, identifying Barrett's esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the det…
Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box Open
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to …
Impact of the global pandemic situation of COVID-19 on the growth of the economic level of private security services in the selected region Open
Research background: The research presented in the article is focused on assessing the impact of measures implemented against the spread of COVID-19 in the conditions of the Slovak Republic. We will focus primarily on curfews, work at home…
View article: Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study
Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study Open
Background The accurate differentiation between T1a and T1b Barrett’s-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on …
View article: 2018 Robotic Scene Segmentation Challenge
2018 Robotic Scene Segmentation Challenge Open
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited ba…
A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology Open
Background and aim The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has…
Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus Open
Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic a…
Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma Open
Made available in DSpace on 2019-10-06T03:06:06Z (GMT). No. of bitstreams: 0 \n Previous issue date: 2019-07-01
Barrett's Esophagus Identification Using Color Co-Occurrence Matrices Open
In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett's Esophagus (BE) and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Pat…