"Multi-class artefact detection in endoscopy" has been recently accepted at IEEE International Symposium on Biomedical Imaging (ISBI 2019).
The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation.
ISBI fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS).
The 2019 meeting will be held in Venice, Italy and will include tutorials, challenges and a scientific program composed of plenary talks, invited special sessions, as well as oral and poster presentations of peer-reviewed papers. High-quality papers are requested containing original contributions to the topics of interest including image formation and reconstruction, image processing and analysis, dynamic imaging, visualization, image quality assessment, machine learning for big image data, and physical, biological, and statistical modeling. Accepted 4-page regular papers will be published in the symposium proceedings published by IEEE and included in IEEE Xplore. To encourage attendance by a broader audience of imaging scientists and clinical professionals and to offer additional presentation opportunities, ISBI 2019 will include special sessions with specific clinical focus and it will continue to have a second track featuring posters selected from 1-page abstract submissions.
Once such submission is Challenge 3: Multi-class artefact detection in video endoscopy by Sharib Ali and Felix Zhou.
Endoscopic Artefact Detection (EAD) is a core challenge in facilitating diagnosis and treatment of diseases in hollow organs. Precise detection of specific artefacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realising reliable computer-assisted tools for improved patient care. The challenge is sub-divided into three tasks:
1.Multi-class artefact detection: Localization of bounding boxes and class labels for 6 artefact classes for given frames.
2.Region segmentation: Precise boundary delineation of detected artefacts.
3.Detection generalization: Detection performance independent of specific data type and source.
They will also be running a workshop after the event with 2-3 keynote speakers, 4-6 talks by top-performing participants and a total of 30-50 presentees (tentative).