Wednesday, 13 April, 8:30am-12:15pm
Organizers
- Babak Ehteshami Bejnordi (Radboud University Medical Center, Nijmegen, NL)
- Oscar Geessink (Radboud University Medical Center, Nijmegen, NL)
- Geert Litjens (Radboud University Medical Center, Nijmegen, NL)
- Jeroen van der Laak (Radboud University Medical Center, Nijmegen, NL)
- Mitko Veta (Eindhoven University of Technology, Eindhoven, NL)
- Nikolas Stathonikos (University Medical Center Utrecht, Utrecht, NL)
Challenge website
Challenge abstract
Digital pathology is a new, rapidly expanding field in medical imaging. In digital pathology, whole-slide scanners are used to digitize glass slides containing tissue specimens at high resolution (up to 160nm per pixel). The availability of digital images has garnered the interest of the medical image analysis community, resulting in increasing numbers of publications on histopathologic image analysis. The first ‘Grand Challenges’ in digital pathology have already taken place, for example the mitosis detection challenge at MICCAI 2013 and atypia challenge at ICPR 2014.
Although these challenges were highly successful, they focused on characterizing nuclei, the smallest unit of information in pathology. Since then, the field has been moving towards grander goals with more potential diagnostic impact: (fully) automated analysis of whole-slide images to detect or grade cancer, to predict prognosis or identify metastases. As such, we feel now is the right time to offer a platform for interested groups to compare strategies and algorithms for a highly meaningful task in histopathology: detection of cancer metastases in lymph nodes in hematoxylin and eosin (H&E) stained whole-slide images. This will be the first challenge using whole-slide images in histopathology. The 2016 challenge will focus on sentinel lymph nodes of breast cancer patients and will provide a large dataset from both the Radboud University Medical Center (Nijmegen, the Netherlands), as well as the University Medical Center Utrecht (Utrecht, the Netherlands).
Other challenges
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Analysis of Images to Detect Abnormalities in Endoscopy (AIDA-E) |
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Skin Lesion Analysis towards Melanoma Detection |
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Lung Nodule Analysis (LUNA) |
