Endoscopy Computer Vision Challenges on Segmentation and Detection (EndoCV2020)
Endoscopy is a widely used clinical procedure for the early detection of numerous cancers (e.g., nasopharyngeal, oesophageal adenocarcinoma, gastric, colorectal cancers, bladder cancer etc.), therapeutic procedures and minimally invasive surgery (e.g., laparoscopy). During this procedure an endoscope is used; a long, thin, rigid or flexible tube with a light source and camera at the tip to visualise the inside of affected organs on an external screen. Quantitative clinical endoscopy analysis is immensely challenging due to inevitable video frame quality degradation from various imaging artefacts to the non-planar geometries and deformations of organs.
After a great success of Endoscopy Artefact Detection challenge (EAD2019), this year EndoCV2020 is introduced with two sub-challenge themes this year:
Sub-challenge I: Endoscopy Artefact Detection and Segmentation (EAD2020)
Sub-challenge II: Endoscopy Disease Detection and Segmentation (EDD2020)
Each sub-challenge consists of detection, semantic segmentation and out-of-sample generalisation tasks for each unique dataset. We intend to release a very comprehensive dataset collected from 7-8 institutions world wide that include:
- Ambroise Paré Hospital of Boulogne-Billancourt, Paris, France
- Botkin Clinical City Hospital, Moscow
- Centro Riferimento Oncologico IRCCS, Aviano, Italy
- ICL Cancer Institute, Nancy, France
- Istituto Oncologico Veneto, Padova, Italy
- John Radcliffe Hospital, Oxford, UK
- University Hospital Vaudois, Lausanne, Switzerland
- -> We are looking for more clinical partners in endoscopy (lower and upper GI)
Participants are allowed to participate in one or both challenges. Datasets built in these challenges may intersect but these are exclusive challenges. You will need to join each sub-challenge separately to participate.
We are looking for sponsors for this challenge, if you would like to support or know more about these challenges then please send an email to Dr. Sharib Ali
Happy participation! Good luck!