chn123/bimcv-analysis

BIMCV-R 500-Series Multi-Model Analysis & Explainability Dataset

This repository contains end-to-end multi-model AI evaluation, lung cancer risk modeling, explainability maps (Grad-CAM), and automated radiology report generation for 500 randomly sampled physician-labeled chest CT series from the cyd0806/BIMCV-R dataset.

The cohort was evaluated across three state-of-the-art chest CT deep learning models:

1. Sybil (5-Seed Ensemble): 1-to-6 year lung cancer risk probability estimation and 3D attention/Grad-CAM explainability maps.
2. Pillar (Pillar0-Sybil-1.5 / YalaLab): 1-to-6 year lung cancer risk prediction using deep multi-window volumetric representations (11 CT windows).
3. Astra (Qwen2.5-VL-7B-Instruct + Merlin): Multimodal 3D vision-language model generating structured chest CT radiology reports (Findings and Impression).

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📁 Repository Structure

├── BIMCV_Sybil_Pillar_Astra_500_Results.xlsx  # Master formatted Excel workbook (3 sheets, 500 cases)
├── bimcv_500_selected_metadata.csv           # Clinical metadata, demographics, and 95 physician pathology labels
├── BIMCV_FN_GRADS/                           # 3D and native Grad-CAM attention matrices (476 series, 16.3 GB)
│   ├── sub-S03406_ses-E77057_run-3_bp-chest_ct_gradcam.npz
│   ├── sub-S03657_ses-E76769_run-1_bp-chest_ct_gradcam.npz
│   └── ... (476 compressed .npz files)
├── astra_generated_reports/                  # 500 full-text structured radiology reports generated by Astra
│   ├── sub-S03406_ses-E77057_run-3_bp-chest_ct_astra_report.txt
│   ├── sub-S03657_ses-E76769_run-1_bp-chest_ct_astra_report.txt
│   └── ... (500 .txt files)
├── results_json/                             # Direct model prediction JSONs
│   ├── sybil_500_scores.json                 # Sybil 1-to-6 year probability scores & runtimes
│   ├── pillar_500_scores.json                # Pillar 1-to-6 year probability scores & runtimes
│   └── astra_500_reports.json                # Astra structured reports & runtimes
└── BIMCV_Sybil_Pillar_Astra_Results.xlsx      # Earlier 10-series prototype workbook

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📊 Master Excel Workbook (BIMCV_Sybil_Pillar_Astra_500_Results.xlsx)

The primary deliverable is a comprehensive, publication-ready Excel spreadsheet containing 500 patient series across three dedicated worksheets:

Sheet 1: Skorlar ve Raporlar (Scores & Clinical Reports)

All key information is placed side-by-side on the exact same row:

• Identifier Data: Row Number, Patient ID (PatientID), Report ID (ReportID), CT Series Filename (ct_path).
• Physician Ground-Truth Labels: Summary string of active findings verified by radiologists.
• Sybil Risk Scores: 1st, 2nd, 3rd, 4th, 5th, and 6th-year estimated lung cancer risk probabilities.
• Pillar Risk Scores: 1st, 2nd, 3rd, 4th, 5th, and 6th-year estimated lung cancer risk probabilities.
• Original Spanish Clinical Report: Hospital ground-truth report (BIMCV_meta.csv).
• Original English Translated Report: Ground-truth report translated into English (Report_en).
• Astra AI Generated Report: Full structured report automatically written by Astra (Qwen2.5-VL-7B).
• Execution Runtimes: Sybil, Pillar, and Astra execution durations in seconds.

Sheet 2: Radyoloji Rapor Kıyaslaması (Full-Text Radiology Report Comparison)

• Side-by-side comparative layout containing Patient ID, Series Name, Physician Findings, Original Spanish Report, English Translated Report, and Astra AI Generated Radiology Report.

Sheet 3: Hekim Etiket Matrisi (95-Pathology Binary Matrix)

• Complete one-hot/binary ground-truth matrix for all 95 clinical findings (COVID-19, adenopathy, consolidation, atelectasis, nodule, emphysema, pleural effusion, etc.) annotated by board-certified radiologists.

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🧠 Explainability Maps: BIMCV_FN_GRADS

The BIMCV_FN_GRADS/ directory contains volumetric Grad-CAM attention matrices for all cases where the Sybil 1-year cancer risk score is below 0.2 (low-risk / potential false-negative screening cases):

• Coverage: Exactly 476 out of 500 series (95.2% of the screening cohort).
• Total Size: 16.3 GB (compressed using NumPy .npz format).
• File Contents:
  • gradcam_native: Shape (25, 16, 16) float32. The exact latent feature-grid attention map computed by the Sybil ensemble (25 depth bins × 16×16 spatial feature grid).
  • gradcam_volume: Shape (N, 512, 512) float16. 3D trilinearly interpolated attention map aligned directly with the original CT slice volume grid ($N$ slices × 512 × 512).
  • image_attention_1: Shape (5, 1, 25, 256) float16. Raw multi-head attention over the 25 spatial slabs across the 5 ensemble seeds.
  • volume_attention_1: Shape (5, 1, 25) float16. Volume attention weights across the 25 depth slabs.
  • scores: float32 array of the 1-to-6 year predicted cancer risk probabilities.

How to Load and Visualize Grad-CAM in Python:

import numpy as np
import matplotlib.pyplot as plt

# Load a Grad-CAM file
grad_file = "BIMCV_FN_GRADS/sub-S04398_ses-E08742_run-2_bp-chest_ct_gradcam.npz"
data = np.load(grad_file)

print("Series:", data["series_name"])
print("6-Year Risk Scores:", data["scores"])
print("Native Grad-CAM Shape:", data["gradcam_native"].shape)    # (25, 16, 16)
print("Volume Grad-CAM Shape:", data["gradcam_volume"].shape)    # (N, 512, 512)

# Visualize maximum intensity projection (MIP) of attention
vol_attention = data["gradcam_volume"]
axial_mip = vol_attention.max(axis=0)

plt.figure(figsize=(6, 6))
plt.imshow(axial_mip, cmap="jet")
plt.title(f"Grad-CAM Axial MIP: {data['series_name']}\nYear 1 Risk: {data['scores'][0]:.4f}")
plt.colorbar(label="Sybil Attention Intensity")
plt.axis("off")
plt.show()

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⚡ Multi-GPU Parallel Inference Architecture

To process 500 full 3D chest CT scans (~41 GB) efficiently, the pipeline was distributed across 3 dedicated NVIDIA A100-SXM4-40GB GPUs:

Model	Framework / Architecture	GPUs Used	Distribution Strategy	Total Duration (500 Scans)
Download	HTTP Range / RemoteZipRaw	8 CPU Workers	Concurrent chunk extraction across 40 zip archives	16.5 mins (41.3 MB/s)
Pillar	Pillar0-Sybil-1.5 (3 Seeds)	GPU 0, 1, 3	3 parallel workers (166 series/GPU)	6.2 mins (~1.5s/scan)
Sybil + Grad-CAM	Sybil Ensemble (5 Models)	GPU 0, 1, 3	3 parallel workers (166 series/GPU)	43.9 mins (~10s/scan)
Astra	Qwen2.5-VL-7B + Merlin	GPU 0, 1, 3	3 parallel workers (166 series/GPU)	79.2 mins (~28s/scan)
Excel & HF Push	OpenPyXL + HfApi	Multi-threaded	Automated workbook styling & artifact upload	3.4 mins
Total Pipeline	End-to-End	3 x A100	Fully Automated	133.2 mins (~2.2 hours)

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📖 Citation & References

@article{mikhael2023sybil,
  title={Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography},
  author={Mikhael, Peter G and Wohlwend, Jeremy and Yala, Adam and others},
  journal={Journal of Clinical Oncology},
  volume={41},
  number={12},
  pages={2191--2200},
  year={2023}
}

@article{yala2024pillar,
  title={Pillar: Foundation Models for Multi-Organ Computed Tomography},
  author={Yala, Adam and others},
  year={2024}
}

@article{astra2024,
  title={Astra: Multimodal Vision-Language Modeling for Automated 3D Chest CT Radiology Report Generation},
  year={2024}
}