Webinars

Are your computer vision models consistently delivering fair and accurate results across all patient groups and data sources? If you're not actively managing bias and batch effects, the answer might surprise you.

Are you tired of seeing your computer vision projects derailed by avoidable mistakes? Discover How to Overcome: - Inconsistent annotations that skew your model's performance. - The lack of baseline models that makes it hard to measure progress. - Data leakage that undermines your model's reliability.

As machine learning practitioners, we've all been there: a model performs brilliantly in testing, only to falter in real-world deployment. Why? Often, it's due to distribution shift.

Discover: - How foundation models are transforming pathology AI development - The power of analyzing gigapixel whole slide images at unprecedented scale - Best practices in getting started with foundation models for pathology