BioSemia began with a single question, commissioned as an experimental project: could tau pathology be read directly from brightfield microscopy, without the stain, and be used to assess the effectiveness of treatment candidates? We assembled a small team of computer vision and machine learning experts to find out. That work became EasyStain.
The success of EasyStain was an eye-opener to everyone involved. With the right combination of AI and biomedical expertise, paired with state-of-the-art techniques fit for the task, the process of doing research can be significantly enhanced in terms of accuracy, speed and cost. Less manual work, fewer errors and fewer tries. More automation, higher accuracy and data-driven conclusions at scale.
Inspired by what EasyStain achieved, and with the wish to make a continued impact on similar or adjacent use cases in preclinical research, BioSemia was founded.
Specialists in computer vision and deep learning, united by the ambition to translate this expertise into real impact on preclinical drug development.
Professor of Artificial Intelligence at the University of Amsterdam, leading research at the intersection of machine learning and information systems. Sets BioSemia's scientific direction and brings a long track record of translating academic AI into systems that ship.
Former Director of Antibody Technologies at VectorY, with extensive experience in immunostaining reagent development and assay design for neuroscience research. Advises BioSemia on the underlying biology, helping ensure the models reflect how these assays are actually used in the lab.
PhD in computer vision from the University of Amsterdam, with prior work on open-ended visual scene understanding in dynamic scenes. Leads engineering across BioSemia's models, turning research-grade systems into pipelines that hold up on real microscopy data.
Associate Professor of Computer Vision at the University of Amsterdam and a leading researcher in deep learning, dynamic vision, and generative AI. Previously co-founded a medical-AI venture in pathology, and now advises on the methodology behind BioSemia's prediction and tracking models.
We would be glad to walk you through either tool on your own images and discuss how it fits your pipeline.
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