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metadata

title: Radiology Report NER API
emoji: 🩺
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0

🩺 Radiology Report NER API

Secure, encrypted medical document analysis with Named Entity Recognition and OCR

🌟 Features

🔐 End-to-End Encryption using NaCl (XSalsa20-Poly1305)
📊 99.94% F-Score NER model accuracy
📄 PDF & Image Support with intelligent text extraction
🖼️ Embedded Image Extraction from medical PDFs
🎯 Entity Detection: ANATOMY & OBSERVATION
⚠️ Critical Finding Detection
💊 Clinical Recommendations
📦 Gzip Compression (25% bandwidth savings)
⚡ EasyOCR Integration for scanned documents

🏗️ Architecture


Client Application
    ↓ (Encrypt + Compress)
FastAPI Server
    ↓ (Decrypt + Decompress)
Text Extraction (PyMuPDF/EasyOCR)
    ↓
spaCy NER Model (99.94% F-score)
    ↓
Post-Processing \& Analysis
    ↓ (Encrypt)
Structured JSON Response

📡 API Endpoints

`POST /analyze-secure`

Secure encrypted endpoint for medical document analysis.

Request Format:


{
    "ciphertext": "base64_encrypted_data",
    "nonce": "base64_nonce"
}

Encrypted Payload Structure:


{
    "filename": "report.pdf",
    "file_data": "base64_encoded_file",
    "file_type": "pdf"
}

Response Format:


{
    "status": "success",
    "ciphertext": "base64_encrypted_response",
    "nonce": "base64_nonce"
}

Decrypted Response Structure:


{
    "status": "success",
    "processing_time": 57.721,
    "filename": "xray_report.pdf",
    "input_type": "pdf",
    "ocr_used": true,
    "ocr_engine": "EasyOCR",
    "raw_text": "Patient report...",
    "text_length": 1022,
    "entities": [
    {
    "text": "lung",
    "label": "ANATOMY",
    "start": 45,
    "end": 49,
    "confidence": 0.998
    }
    ],
    "images": [
    {
    "page": 1,
    "format": "JPEG",
    "width": 800,
    "height": 600,
    "data": "data:image/jpeg;base64,..."
    }
    ],
    "structured_report": {
    "anatomy": ["lung", "heart", "chest"],
    "all_observations": ["clear", "normal"],
    "positive_findings": [],
    "negative_findings": ["clear", "normal"],
    "critical_findings": []
    },
    "summary": {
    "total_entities": 12,
    "anatomy_count": 6,
    "observations_count": 6,
    "has_critical_findings": false,
    "has_abnormalities": false
    },
    "recommendations": [
    "No significant abnormalities detected"
    ]
}

`GET /health`

Health check endpoint.

Response:


{
    "status": "healthy",
    "model_loaded": true,
    "model_pipeline": ["tok2vec", "ner"],
    "model_labels": ["ANATOMY", "OBSERVATION"],
    "ocr_engine": "EasyOCR",
    "encryption": "NaCl (XSalsa20-Poly1305)",
    "compression": "gzip",
    "version": "1.0.0"
}

🔒 Security

Encryption Details

Algorithm: NaCl (Networking and Cryptography library)
Cipher: XSalsa20 stream cipher
Authentication: Poly1305 MAC
Key Derivation: PBKDF2 with SHA-256
Nonce: 24 bytes (randomly generated per request)

Compression

Algorithm: gzip
Average Savings: 25-30% bandwidth reduction
Applied: Before encryption on client, after decryption on server

Data Flow

Client compresses payload with gzip
Client encrypts compressed data with NaCl
Server decrypts and decompresses
Server processes medical document
Server encrypts response
Client decrypts response

🚀 Deployment

HuggingFace Spaces

This API is deployed on HuggingFace Spaces using Docker.

Local Development



# Clone repository

git clone <your-repo-url>
cd radiology-ner-api

# Create virtual environment

python -m venv venv
source venv/bin/activate  \# On Windows: venv\Scripts\activate

# Install dependencies

pip install -r requirements.txt

# Download spaCy model

python -m spacy download en_core_web_sm

# Run server

uvicorn app.main:app --host 0.0.0.0 --port 7860

Environment Variables


ENCRYPTION_KEY=your-secret-encryption-key-min-32-chars
MODEL_PATH=./models/xray_ner_best
HOST=0.0.0.0
PORT=7860

📊 Model Performance

Metric	Score
F-Score	99.94%
Precision	99.92%
Recall	99.96%
Training Samples	2,674 reports
Entity Types	2 (ANATOMY, OBSERVATION)

Training Data

Dataset: Indiana University Chest X-Ray Collection
Reports: 2,674 radiology reports
Annotations: Manual entity labeling
Framework: spaCy v3.7
Architecture: HashEmbedCNN

🛠️ Technology Stack

Backend: FastAPI 0.104.1
NER: spaCy 3.7
OCR: EasyOCR
PDF Processing: PyMuPDF (fitz)
Image Processing: OpenCV, Pillow
Encryption: PyNaCl
Compression: gzip
Deployment: Docker, HuggingFace Spaces

📝 Client Implementation Example


import base64
import gzip
import json
import requests
from nacl.secret import SecretBox
from nacl.utils import random

SECRET_KEY = "your-encryption-key"

def encrypt_file(file_path, file_type):
\# Read file
with open(file_path, 'rb') as f:
file_data = base64.b64encode(f.read()).decode()

    # Create payload
    payload = {
        "filename": file_path.split('/')[-1],
        "file_data": file_data,
        "file_type": file_type
    }
    
    # Compress
    compressed = gzip.compress(json.dumps(payload).encode())
    compressed_b64 = base64.b64encode(compressed).decode()
    
    # Encrypt
    key = derive_key(SECRET_KEY)
    box = SecretBox(key)
    nonce = random(SecretBox.NONCE_SIZE)
    encrypted = box.encrypt(compressed_b64.encode(), nonce)
    
    return {
        "ciphertext": base64.b64encode(encrypted[24:]).decode(),
        "nonce": base64.b64encode(nonce).decode()
    }
    
# Send request

encrypted_payload = encrypt_file("report.pdf", "pdf")
response = requests.post(
"https://your-space.hf.space/analyze-secure",
json=encrypted_payload
)

# Decrypt response

result = decrypt_response(response.json(), SECRET_KEY)
print(result)

🎯 Use Cases

Clinical Decision Support: Extract structured data from radiology reports
Medical Record Digitization: OCR for scanned medical documents
Research Analytics: Automated entity extraction for medical research
DPDPA Compliance: Secure processing of sensitive medical data (Digital Personal Data Protection Act, 2023)
Telemedicine: Remote radiology report analysis under Telemedicine Practice Guidelines
Healthcare AI Research: Supporting AI/ML research in Indian healthcare sector

📄 License & Compliance

License: Apache License 2.0 - see LICENSE file for details

Indian Regulations Compliance:

Digital Personal Data Protection Act (DPDPA), 2023: End-to-end encryption ensures data protection
Information Technology Act, 2000: Secure data transmission and storage
Telemedicine Practice Guidelines, 2020: Compliant with MCI telemedicine regulations
Clinical Establishments Act, 2010: Suitable for registered clinical establishments

Note: Users must ensure compliance with:

State-specific medical data regulations
Medical Council of India (MCI) guidelines
National Health Authority (NHA) standards for digital health
Ayushman Bharat Digital Mission (ABDM) integration requirements

⚠️ Disclaimer

This API is designed for research, educational, and assistive purposes only. It is compliant with Indian digital health regulations including DPDPA 2023 and Telemedicine Practice Guidelines.

Important Notice:

This tool does NOT replace professional medical diagnosis or treatment
All analysis must be reviewed and validated by qualified medical practitioners registered with Medical Council of India (MCI) or State Medical Councils
Users must comply with Indian medical data protection laws (DPDPA 2023)
Healthcare providers must maintain proper patient consent as per Indian regulations
For clinical use, ensure compliance with Clinical Establishments Act and relevant state laws

Always consult qualified and registered healthcare professionals for medical advice and diagnosis.

Built with ❤️ for the medical AI community