Jedi Labs

Medical Image Classification

Using Weaviate vector search capabilities combined with a classification model to analyze and categorize medical images (e.g., X-rays, CT scans) for detecting anomalies like pneumonia or tumors.

Healthcare

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Core Capabilities

  • High-accuracy image classification (via external model)
  • Similarity search for comparable cases (Weaviate)
  • Supports various medical imaging formats (via preprocessing)
  • Content-based image retrieval (Weaviate)

    • Solution Architecture

    A pipeline involving DICOM/image data ingestion and preprocessing, feature extraction (e.g., via CNN), vectorization, indexing in Weaviate, similarity search, and classification/reporting.

    Implementation Flow
    Step Classification & Reporting:The Classification Service uses the similarity search results to make a classification decision and generate a report.

    Output: Structured report (e.g., JSON, PDF) with findings.

    Step Clinical Review:A healthcare professional reviews the AI-generated report.

    Final decision remains with the human expert.

    Step Image Acquisition:Medical image is acquired from the source system.

    Input: DICOM, PNG, JPG etc.

    Step Preprocessing & Feature Extraction:Image is processed, and a feature vector is extracted.

    Output: Numerical vector representing the image.

    Step Query / Similarity Search:For a new image (or existing one), its vector is used to query Weaviate for similar image vectors.

    Weaviate returns IDs and distances of the most similar images.

    Step Vector Indexing:The feature vector and associated metadata are added/updated in the Weaviate instance.

    Makes the image searchable based on content.

    React Flow mini map
    React Flow
    System Components
    Classification & Reporting Service

    Queries Weaviate for similar images, potentially uses retrieved neighbors to inform a classification decision (e.g., k-NN classification or feeding neighbors to another model), and generates reports.

    Medical Image Preprocessor & Feature Extractor

    Loads images, performs normalization/resizing, and extracts meaningful features using a deep learning model (e.g., CNN).

    Weaviate Vector Database Instance

    Stores and indexes the vector representations of the medical images along with relevant metadata.

    Industry Context & Applications

    Jedi Labs Applications
    Agentic Personalized Learning Orchestration
    Engine: aiAnalysisEngine
    100x Engineering Acceleration
    Engine: aiAnalysisEngine

    Implementation & Success Metrics

    Implementation Requirements
    • Access to DICOM or other medical image formats
    • GPU-accelerated environment for model inference
    • Secure storage for patient data (HIPAA compliance considerations)
    • Integration with PACS or EMR systems (optional)
    • Trained image classification model (e.g., CNN)
    Success Metrics
    Classification accuracy (e.g., >95% for specific conditions)
    Reduction in diagnostic time
    Improved detection rate for subtle anomalies
    User satisfaction score from radiologists/clinicians
    System Integration Points
    Picture Archiving and Communication System (PACS)
    Electronic Medical Record (EMR) / Electronic Health Record (EHR)
    Radiology Information System (RIS)
    Clinical Decision Support Systems (CDSS)

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    Let our experts help you implement Medical Image Classification with our proven methodology and cutting-edge technology stack.

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    Medical Image Classification

    Overview

    Using Weaviate vector search capabilities combined with a classification model to analyze and categorize medical images (e.g., X-rays, CT scans) for detecting anomalies like pneumonia or tumors.

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