H-optimus-0
| Property | Value |
|---|---|
| Parameter Count | 1.1B |
| Model Type | Vision Transformer |
| Developer | Bioptimus |
| Model Hub | Hugging Face |
What is H-optimus-0?
H-optimus-0 is a state-of-the-art foundation model specifically designed for histological image analysis. Developed by Bioptimus, this transformer-based architecture has been trained on an extensive proprietary dataset of over 500,000 H&E stained whole slide histology images, making it a powerful tool for medical image analysis.
Implementation Details
The model operates on 224x224 pixel images captured at 0.5 microns per pixel resolution. It employs a vision transformer architecture with specific normalization parameters (mean: 0.707223, 0.578729, 0.703617; std: 0.211883, 0.230117, 0.177517) and outputs a 1536-dimensional feature vector for each image.
- Built using the TIMM library for efficient implementation
- Supports CUDA acceleration and mixed precision inference
- Implements automatic feature extraction pipeline
Core Capabilities
- Mutation prediction from histological images
- Survival analysis based on tissue samples
- Automated tissue classification
- Feature extraction for downstream applications
Frequently Asked Questions
Q: What makes this model unique?
H-optimus-0 stands out due to its extensive training on a large-scale proprietary histology dataset and its specialized architecture for medical image analysis. The model's ability to extract meaningful features from H&E stained slides makes it particularly valuable for pathology applications.
Q: What are the recommended use cases?
The model is specifically designed for histopathology analysis, including mutation prediction, survival analysis, and tissue classification. It's particularly useful for researchers and medical professionals working with H&E stained whole slide images who need to extract meaningful features for downstream analysis.
