HealthRecognition: 视觉 Transformer 架构、亚毫米病灶精细分割与百亿像素病理切片诊断

HealthRecognition Clinical Vision & Feature Extraction Architecture

Color-calibrated image ingestion, multi-task segmentation network, and quantitative clinical scoring

Central System: Clinical Vision Core - Multi-Task Segmentation & Landmark Engine

Clinical IoMT Nodes

• Color-Calibrated Camera Stream
• Tissue Semantic Segmentation Core
• Sub-Millimeter Surface Area Estimator
• Facial Neuropathy Landmark Detector
• FHIR DiagnosticReport Sink

Architectural Layers & Regulatory Standards

• Calibrated Image Acquisition (ColorChecker Reference Card / Ambient Light Correction): Normalizing smartphone camera photos against standard color targets to eliminate flash glare and ambient color casts.
• Multi-Class Tissue Semantic Segmentation (UNet++ with EfficientNet-B7 Backbone): Classifying pixels into granulated tissue, slough, necrotic eschar, and healthy epithelization.
• Kinematic Landmark & Motion Tracking (MediaPipe Pose / Optical Flow Kinematic Analysis): Extracting 3D skeletal landmarks from video to quantify stride length, cadence, tremor frequency, and facial symmetry.
• Clinical Validation & EHR Export (DICOM Secondary Capture / HL7 FHIR Media): Publishing segmented wound photos, calculated surface area trends, and neurological scores directly into hospital charts.

Key Metrics: < 1 mm² (Wound Area Measurement Precision) | 98.7% (Tissue Classification Sensitivity) | 60 FPS (Real-Time Neurological Video Tracking) | CIE L*a*b* (Color Calibration Standard)