templates/about.html

{% extends "base.html" %}

{% block title %}About — AI Medical Intelligence Pipeline{% endblock %}

{% block content %}
<section class="hero">
  <div class="hero-text">
    <h1>About This System</h1>
    <p>
      AI Medical Intelligence Pipeline for CT Scan Analysis with Explainability
      and Clinical Reporting
    </p>
  </div>
</section>

<!-- System Overview -->
<section class="panel">
  <h3>System Overview</h3>
  <p>
    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.
  </p>
  <div class="arch-flow">
    <div class="arch-step">
      <div class="arch-num">1</div>
      <div class="arch-label">CT Brain Image Input</div>
    </div>
    <div class="arch-arrow">→</div>
    <div class="arch-step">
      <div class="arch-num">2</div>
      <div class="arch-label">Preprocessing &amp; CT Windowing</div>
    </div>
    <div class="arch-arrow">→</div>
    <div class="arch-step">
      <div class="arch-num">3</div>
      <div class="arch-label">2.5D Detection (EfficientNet-B4)</div>
    </div>
    <div class="arch-arrow">→</div>
    <div class="arch-step">
      <div class="arch-num">4</div>
      <div class="arch-label">Grad-CAM Explainability</div>
    </div>
    <div class="arch-arrow">→</div>
    <div class="arch-step">
      <div class="arch-num">5</div>
      <div class="arch-label">Confidence Calibration</div>
    </div>
    <div class="arch-arrow">→</div>
    <div class="arch-step">
      <div class="arch-num">6</div>
      <div class="arch-label">Clinical Report</div>
    </div>
  </div>
</section>

<!-- Technical Details -->
<section class="about-grid">
  <article class="panel">
    <h3>Model Architecture</h3>
    <div class="kv-group">
      <div class="kv">
        <span>Architecture</span><strong>EfficientNet-B4 (timm)</strong>
      </div>
      <div class="kv">
        <span>Input Representation</span><strong>2.5D (prev/center/next)</strong>
      </div>
      <div class="kv">
        <span>Channels</span><strong>9 (3 CT windows × 3 slices)</strong>
      </div>
      <div class="kv"><span>Outputs</span><strong>6 heads (any + 5 subtypes)</strong></div>
      <div class="kv">
        <span>Inference Strategy</span><strong>5-fold ensemble (logit averaging)</strong>
      </div>
    </div>
  </article>

  <article class="panel">
    <h3>CT Preprocessing</h3>
    <div class="kv-group">
      <div class="kv">
        <span>Brain Window</span><strong>WC=40, WW=80</strong>
      </div>
      <div class="kv">
        <span>Subdural Window</span><strong>WC=75, WW=215</strong>
      </div>
      <div class="kv">
        <span>Soft Tissue Window</span><strong>WC=40, WW=380</strong>
      </div>
      <div class="kv">
        <span>Channels</span><strong>3 (one per window)</strong>
      </div>
      <div class="kv">
        <span>Format</span><strong>DICOM → HU → windowed RGB</strong>
      </div>
    </div>
  </article>

  <article class="panel">
    <h3>Calibration</h3>
    <div class="kv-group">
      <div class="kv">
        <span>Method</span
        ><strong>{{ calib.get('method', calib.get('best_method', 'N/A')) }}</strong>
      </div>
      {% if calib %}
      <div class="kv">
        <span>Temperature</span
        ><strong>{{ '%.4f'|format(calib.temperature) }}</strong>
      </div>
      <div class="kv">
        <span>Threshold</span
        ><strong>{{ '%.4f'|format(calib.calibrated_threshold) }}</strong>
      </div>
      {% endif %}
      <div class="kv">
        <span>ECE (Raw → Calibrated)</span
        ><strong>{{ '%.4f'|format(calib.get('raw_ece', 0.0)) }} → {{ '%.4f'|format(calib.get('cal_ece', 0.0)) }}</strong>
      </div>
      <div class="kv">
        <span>Bands</span
        ><strong>
          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 (&lt;{{ '%.2f'|format(calib.get('low_threshold', 0.3)) }})
        </strong>
      </div>
    </div>
  </article>

  <article class="panel">
    <h3>Explainability</h3>
    <div class="kv-group">
      <div class="kv"><span>Method</span><strong>Grad-CAM</strong></div>
      <div class="kv">
        <span>Target Layer</span><strong>Last convolutional block</strong>
      </div>
      <div class="kv">
        <span>Output</span><strong>Heatmap overlay on input</strong>
      </div>
      <div class="kv">
        <span>Purpose</span><strong>Visual evidence for review</strong>
      </div>
    </div>
  </article>
</section>

<!-- Confidence-Aware Triage -->
<section class="panel" style="margin-top: 16px">
  <h3>Confidence-Aware Triage System</h3>
  <p>
    Instead of a simple binary output, the system incorporates prediction
    confidence into a three-band triage workflow:
  </p>

  <div class="triage-grid">
    <div class="triage-card triage-high">
      <div class="triage-header">
        <span class="badge badge-high">HIGH</span>
        <span>≥ {{ '%.2f'|format(calib.get('high_threshold', 0.7)) }} calibrated probability</span>
      </div>
      <p><strong>If positive:</strong> Urgent radiologist review recommended</p>
      <p><strong>If negative:</strong> Standard workflow — no urgent action</p>
    </div>

    <div class="triage-card triage-medium">
      <div class="triage-header">
        <span class="badge badge-medium">MEDIUM</span>
        <span>{{ '%.2f'|format(calib.get('low_threshold', 0.3)) }} – {{ '%.2f'|format(calib.get('high_threshold', 0.7)) }}</span>
      </div>
      <p>
        <strong>If positive:</strong> Prioritised radiologist review recommended
      </p>
      <p>
        <strong>If negative:</strong> Standard workflow — manual review if
        clinically indicated
      </p>
    </div>

    <div class="triage-card triage-low">
      <div class="triage-header">
        <span class="badge badge-low">LOW</span>
        <span>&lt; {{ '%.2f'|format(calib.get('low_threshold', 0.3)) }}</span>
      </div>
      <p>
        <strong>If positive:</strong> Radiologist review recommended — low
        confidence
      </p>
      <p>
        <strong>If negative:</strong> Manual review recommended — model
        uncertainty high
      </p>
    </div>
  </div>
</section>

<!-- Dataset -->
<section class="panel" style="margin-top: 16px">
  <h3>Dataset</h3>
  <div class="kv-group" style="max-width: 600px">
    <div class="kv">
      <span>Source</span><strong>RSNA Intracranial Hemorrhage Detection</strong>
    </div>
    <div class="kv">
      <span>Modality</span><strong>CT brain (axial slices)</strong>
    </div>
    <div class="kv"><span>Format</span><strong>DICOM</strong></div>
    <div class="kv">
      <span>Task</span><strong>Any-hemorrhage screening + subtype-aware outputs</strong>
    </div>
  </div>
</section>

<!-- Ethical Considerations -->
<section class="panel" style="margin-top: 16px">
  <h3>Ethical Considerations &amp; Limitations</h3>

  <div class="ethics-columns">
    <div>
      <h4>This System Is:</h4>
      <ul class="check-list">
        <li>A screening and decision-support tool</li>
        <li>Designed to assist, not replace, medical professionals</li>
        <li>Transparent via Grad-CAM visual evidence</li>
        <li>Calibrated for reliable confidence scores</li>
        <li>Built on publicly available, ethically sourced data</li>
      </ul>
    </div>
    <div>
      <h4>This System Is NOT:</h4>
      <ul class="cross-list">
        <li>A diagnostic device or medical diagnosis tool</li>
        <li>A replacement for qualified radiologist review</li>
        <li>Cleared for standalone clinical deployment</li>
        <li>A substitute for clinical subtype confirmation</li>
        <li>Validated for real-time hospital use</li>
      </ul>
    </div>
  </div>
</section>

<!-- Disclaimer -->
<section class="disclaimer-box" style="margin-top: 16px">
  <strong>Important Disclaimer:</strong>
  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.
</section>

<!-- Technology Stack -->
<section class="panel" style="margin-top: 16px">
  <h3>Technology Stack</h3>
  <div class="tech-tags">
    <span class="tech-tag">Python</span>
    <span class="tech-tag">PyTorch</span>
    <span class="tech-tag">EfficientNet-B4</span>
    <span class="tech-tag">timm</span>
    <span class="tech-tag">2.5D Context</span>
    <span class="tech-tag">5-Fold Ensemble</span>
    <span class="tech-tag">Isotonic Calibration</span>
    <span class="tech-tag">OpenCV</span>
    <span class="tech-tag">NumPy</span>
    <span class="tech-tag">Pandas</span>
    <span class="tech-tag">Matplotlib</span>
    <span class="tech-tag">Grad-CAM</span>
    <span class="tech-tag">Flask</span>
    <span class="tech-tag">pydicom</span>
    <span class="tech-tag">scikit-learn</span>
  </div>
</section>
{% endblock %}