Today we release of the sixth in our series of short videos demonstrating how the EOSC Federation enables cross-disciplinary science.
Advances in digital pathology are transforming cancer diagnosis by enabling high-resolution imaging of tissue samples and the application of artificial intelligence (AI) for clinical decision support.
Prostate cancer, one of the most prevalent malignancies among men worldwide, represents a critical case for early and accurate diagnosis, as survival rates are strongly tied to timely detection and treatment. The objective of the multi-centric validation (MCVAL) of AI models for prostate-cancer screening use case, coordinated by the EOSC Node BBMRI-ERIC, is to create a secure environment in which AI models for prostate cancer screening can be validated using data from different hospitals.
Rather than building new diagnostic systems from scratch, the project focuses on testing an existing model trained on whole-slide images and assessing how well it performs when applied to data processed elsewhere.
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