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About This System

AI Medical Intelligence Pipeline for CT Scan Analysis with Explainability and Clinical Reporting

System Overview

This is an AI medical intelligence pipeline designed to analyze CT brain scans for intracranial hemorrhage (ICH). It combines deep learning with visual explainability, confidence calibration, and structured clinical reporting to support — not replace — medical decision-making.

1
CT Brain Image Input
→
2
Preprocessing & CT Windowing
→
3
2.5D Detection (EfficientNet-B4)
→
4
Grad-CAM Explainability
→
5
Confidence Calibration
→
6
Clinical Report

Model Architecture

ArchitectureEfficientNet-B4 (timm)
Input Representation2.5D (prev/center/next)
Channels9 (3 CT windows × 3 slices)
Outputs6 heads (any + 5 subtypes)
Inference Strategy5-fold ensemble (logit averaging)

CT Preprocessing

Brain WindowWC=40, WW=80
Subdural WindowWC=75, WW=215
Soft Tissue WindowWC=40, WW=380
Channels3 (one per window)
FormatDICOM → HU → windowed RGB

Calibration

Method{{ calib.get('method', calib.get('best_method', 'N/A')) }}
{% if calib %}
Temperature{{ '%.4f'|format(calib.temperature) }}
Threshold{{ '%.4f'|format(calib.calibrated_threshold) }}
{% endif %}
ECE (Raw → Calibrated){{ '%.4f'|format(calib.get('raw_ece', 0.0)) }} → {{ '%.4f'|format(calib.get('cal_ece', 0.0)) }}
Bands HIGH (≥{{ '%.2f'|format(calib.get('high_threshold', 0.7)) }}) · MEDIUM ({{ '%.2f'|format(calib.get('low_threshold', 0.3)) }}–{{ '%.2f'|format(calib.get('high_threshold', 0.7)) }}) · LOW (<{{ '%.2f'|format(calib.get('low_threshold', 0.3)) }})

Explainability

MethodGrad-CAM
Target LayerLast convolutional block
OutputHeatmap overlay on input
PurposeVisual evidence for review

Confidence-Aware Triage System

Instead of a simple binary output, the system incorporates prediction confidence into a three-band triage workflow:

HIGH ≥ {{ '%.2f'|format(calib.get('high_threshold', 0.7)) }} calibrated probability

If positive: Urgent radiologist review recommended

If negative: Standard workflow — no urgent action

MEDIUM {{ '%.2f'|format(calib.get('low_threshold', 0.3)) }} – {{ '%.2f'|format(calib.get('high_threshold', 0.7)) }}

If positive: Prioritised radiologist review recommended

If negative: Standard workflow — manual review if clinically indicated

LOW < {{ '%.2f'|format(calib.get('low_threshold', 0.3)) }}

If positive: Radiologist review recommended — low confidence

If negative: Manual review recommended — model uncertainty high

Dataset

SourceRSNA Intracranial Hemorrhage Detection
ModalityCT brain (axial slices)
FormatDICOM
TaskAny-hemorrhage screening + subtype-aware outputs

Ethical Considerations & Limitations

This System Is:

  • A screening and decision-support tool
  • Designed to assist, not replace, medical professionals
  • Transparent via Grad-CAM visual evidence
  • Calibrated for reliable confidence scores
  • Built on publicly available, ethically sourced data

This System Is NOT:

  • A diagnostic device or medical diagnosis tool
  • A replacement for qualified radiologist review
  • Cleared for standalone clinical deployment
  • A substitute for clinical subtype confirmation
  • Validated for real-time hospital use
Important Disclaimer: This system is produced by an AI-assisted screening tool and does NOT constitute a medical diagnosis. All screening findings must be reviewed and confirmed by a qualified, licensed medical professional before any clinical decision is made. The system is intended solely as a decision-support aid in a screening workflow and is not cleared for standalone diagnostic use.

Technology Stack

Python PyTorch EfficientNet-B4 timm 2.5D Context 5-Fold Ensemble Isotonic Calibration OpenCV NumPy Pandas Matplotlib Grad-CAM Flask pydicom scikit-learn
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