A5077390302- Artificial Intelligence in Healthcare and Education
- Surgical Simulation and Training
- Radiomics and Machine Learning in Medical Imaging
- Biomedical Text Mining and Ontologies
- Augmented Reality Applications
- Semantic Web and Ontologies
- Advanced Radiotherapy Techniques
- Cardiac, Anesthesia and Surgical Outcomes
- Soft Robotics and Applications
- Ultrasound Imaging and Elastography
- Biomedical and Engineering Education
- Anatomy and Medical Technology
- Delphi Technique in Research
- Mobile Crowdsensing and Crowdsourcing
- AI in cancer detection
- Scientific Computing and Data Management
- Advanced X-ray and CT Imaging
- Photoacoustic and Ultrasonic Imaging
- COVID-19 diagnosis using AI
- Clinical practice guidelines implementation
- Electrical and Bioimpedance Tomography
- Innovations in Medical Education
- Data Quality and Management
- Atomic and Subatomic Physics Research
- Health and Medical Research Impacts
German Cancer Research Center
2014-2024
Heidelberg University
2013-2024
National Center for Tumor Diseases
2024
DKFZ-ZMBH Alliance
2013-2020
Metropolitan University
2018
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical common practices related to organization has not yet been performed. In this paper, we present comprehensive conducted up now. We demonstrate importance and show lack quality control consequences. First, reproducibility interpretation results often hampered as only fraction relevant information typically provided. Second, rank...
Accurate segmentations in medical images are the foundations for various clinical applications. Advances machine learning-based techniques show great potential automatic image segmentation, but these usually require a huge amount of accurately annotated reference training. The guiding hypothesis this paper was that crowd-algorithm collaboration could evolve as key technique large-scale data annotation. As an initial step toward goal, we evaluated performance untrained individuals to detect...
In the surgical domain, individual clinical experience, which is derived in large part from past cases, plays an important role treatment decision process. Simultaneously surgeon has to keep track of a amount data, emerging number heterogeneous systems during all phases treatment. This complemented with constantly growing knowledge studies and literature. To recall this vast information at right moment poses challenge that should be supported by adequate technology. While many tools projects...
Surgical data science is a scientific discipline with the objective of improving quality interventional healthcare and its value through capturing, organization, analysis, modeling data. The goal 1st workshop on Data Science was to bring together researchers working diverse topics in surgical order discuss existing challenges, potential standards new research directions field. Inspired by current open space think tank formats, it organized June 2016 Heidelberg. While first day workshop,...
Every year approximately 234 million major surgeries are performed, leading to plentiful, highly diverse data. This is accompanied by a matching number of novel algorithms for the surgical domain. To garner all benefits data science it necessary have an unambiguous, shared understanding and includes inputs outputs thus their function, but also semantic content, i.e. meaning such as patient parameters. We therefore propose establishment new ontology in science. Such can be used provide common...
Visualization of anatomical data for disease diagnosis, surgical planning, or orientation during interventional therapy is an integral part modern health care. However, as information typically shown on monitors provided by a radiological work station, the physician has to mentally transfer internal structures screen patient. To address this issue, we recently presented new approach on-patient visualization 3D medical images, which combines concept augmented reality (AR) with intuitive...
Computer-assisted interventions (CAI) typically require localization (tracking) of surgical instruments and the patient.For ultrasound (US)-guided interventions, a new compact electromagnetic (EM) field generator enables construction combined modality which allows for both, EM tracking US imaging with one handheld device.In this study, we present research prototype such device conduct accuracy assessments in clinical suite.The results show robust device, emerges as promising component...