Designed to help medical AI companies build robust and cost-effective medical imaging AI solutions

What is the

FIVEBRANE

FIVEBRANEis a development platform designed for medical imaging AI teams.

It combines the collaborative spirit of version control system hub with compliance-aware workflows, smart data infrastructure, and clinical expertise-empowering Med-Tech AI teams to build reliable, robust, compliant, and cost-effective solutions.

Why chooseFIVEBRANE

Shorten Time to Market

From data to deployment-accelerated and de-risked.

FiveBrane platform empowers Med-Tech AI teams with smart data access, bias detection, and debugging tools, all built with regulatory compliancies in mind. Unlock siloed data and collaborate seamlessly with doctors on tap. Reduce bias, increase robustness, and get to market faster with AI that’s designed to perform in the real world.

Disagree and Commit: Why "Get More Data" is a Waste of Money?

The scale is not a strategy. It is an outcome. The same is true in AI.The common mandate is to 'get more data.' This is a high-cost, low-rigor approach.It misunderstands the fundamental mechanism.

Turn Patch Embeddings into a Bias and Robustness Detector

When you analyze which patches are semantically or visually similar, you're essentiallyexploring how the model 'understands' the visual world. This can be very useful for detecting bias in data/AI.

What are Image Patch Embeddings, and how do They help shorten Time to Market?

An image patch embedding is a fixed-length vector that represents a small patch of an image.For example, a 224×224 image might be split into 16×16 patches, and each patch is embedded into a vector(e.g., 768-dimensional) that encodes its visual features.