Endoscopic Artefact Detection (EAD) is a core problem and needed for realising robust computer-assisted tools. The EAD challenge has 3 tasks: 1) Multi-class artefact detection, 2) Region segmentation, 3) Detection generalisation.
Stereo Correspondence and Reconstruction of Endoscopic Data
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Stereo Correspondence and Reconstruction of Endoscopic Data
This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.
EndoCV2021
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Endoscopy Computer Vision Challenge 2021
FLARE21
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Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge
STOIC2021 - COVID-19 AI Challenge
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COVID-19 Artificial Intelligence Challenge: automated diagnosis, and prognostic evaluation based on computed tomography
EndoCV2022
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Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data
SurgT: Surgical Tracking
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This challenge consists of surgical videos with a target bounding box and the participants are expected to develop visual tracking algorithms to estimate the trajectory of the bounding box throughout the video-sequence.
Preoperative to Intraoperative Laparoscopy Fusion
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Preoperative to Intraoperative Laparoscopy Fusion
Cell Segmentation in Multi-modality Microscopy Images
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Weakly Supervised Cell Segmentation in Multi-modality High-resolution Microscopy Images
