Visual encoder for pathology slides
Learn from whole-slide pathology and tile-level tissue structure through large-scale representation learning.
Platform
Model architecture
Epithelia AI is built around a foundation-model architecture for epithelial biology.
At the visual layer, the system learns from whole-slide pathology and tile-level tissue structure using large-scale representation learning. At the multimodal layer, these visual representations can be aligned with text, molecular data, and structured clinical context. At the application layer, the platform supports downstream tasks such as classification, retrieval, report assistance, biomarker prediction, and tissue-state modeling.
The architectural idea is simple.
First, learn a general representation of epithelial tissue.
Then, adapt that representation across high-value oncology tasks.
Architecture blocks
Tissue embedding and representation layer
Build reusable epithelial representations that preserve morphology, architecture, and tissue state.
Multimodal fusion layer
Align visual tissue representations with text, molecular data, and structured clinical context.
Task-specific application heads
Adapt the learned tissue representation to classification, retrieval, biomarker prediction, report assistance, and tissue-state modeling.
A reusable tissue intelligence layer
The platform is meant to learn once and apply many times.
That is what gives Pathelium a path from a focused epithelial model program to a broader oncology intelligence stack.
See where the architecture can be applied
The model stack is designed to support high-value applications across oncology research and future clinical workflows.