Technical Deep Dive

OrenAI
Architecture

Multimodal sensor fusion for early-stage breast cancer detection. Vision Transformers, CNN ensembles, and real-time edge inference.

OrenAI

Multimodal Perception Stack

System Architecture

Layer 1

Multimodal Sensor Fusion

Simultaneous processing of LiDAR point clouds, thermal imaging arrays, RGB camera streams, and DICOM medical imaging. Our fusion layer creates a unified spatial representation by aligning temporal sequences across all modalities.

  • •LiDAR: 360° spatial mapping at 10Hz
  • •Thermal: Temperature differential detection
  • •RGB: High-resolution optical imaging
  • •DICOM: Medical imaging standard integration
FUSION
LiDAR
Thermal
RGB
DICOM
ViT
Vision Transformer
Layer 2

Vision Transformers + CNN Ensembles

Hybrid architecture combining self-attention mechanisms from Vision Transformers with convolutional feature extraction. The ensemble approach leverages both global context (ViT) and local feature detection (CNN) for robust anomaly identification.

  • •ViT: Global spatial relationships
  • •CNN: Local feature extraction
  • •Ensemble: Weighted voting mechanism
Layer 3

Real-Time Edge Inference

Optimized for sub-15ms inference on edge devices using TensorRT quantization and model pruning. The system processes fused sensor data in real-time, enabling immediate clinical feedback without cloud dependency.

<15ms
Inference Time
Edge
Deployment
EDGE
Real-Time Processing

Technology Stack

PyTorch

Model training & inference

TensorRT

Edge optimization

OpenCV

Image processing

Monai

Medical imaging

Unity AR

3D visualization

Whisper API

Voice commands

NVIDIA Jetson

Edge hardware

DICOM

Medical standard

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