MICCAI Special Interest Group on Biomedical Image Analysis Challenges
Company

MICCAI Special Interest Group on Biomedical Image Analysis Challenges

Biotechnology Research The Discrepancy Between the Impact of Challenges and the Quality (Control) 126 employees
Employees
126

MICCAI Special Interest Group on Biomedical Image Analysis Challenges Overview

Headquarters
The Discrepancy Between the Impact of Challenges and the Quality (Control)
Website
miccai.org
Industry
Biotechnology Research
Employees
126
Founded
2021
NAICS
Research and Development in Biotechnology (except Nanobiotechnology)

About MICCAI Special Interest Group on Biomedical Image Analysis Challenges

SIG for Challenges The current gold standard for fair performance evaluation in the field of medical image analysis are international competitions (‘challenges’) enabling algorithmic benchmarking in a controlled environment while simulating real-world conditions. The mission of the Special Interest Group on Biomedical Image Analysis Challenges is to unite different societies and scientific communities to establish best practices and raise the level of quality in image analysis validation, with a special focus on biomedical image analysis challenges. Our mission is two-fold: First, we strive for the professionalization and definitive introduction of best practices into the field of biomedical image analysis. To this end, we develop infrastructure and promote standardization in the hosting, communication, and evaluation of biomedical image analysis challenges. Committing to the ‘Findability, Accessibility, Interoperability, and Reuse (FAIR) Guiding Principles for scientific data management and stewardship’, we aim to enhance transparency and access to data and methods. Second, we strive to educate scientific communities on all aspects related to conducting challenges, with a particular focus on their organization, data quality control, and evaluation using appropriate statistics and metrics. We encourage and enable open knowledge transfer and collaboration between diverse scientific communities, and lead community discussions towards consensus on important topics, such as AI-readiness of data. We commit to our work being continuously informed by community feedback.

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