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license: apache-2.0
tags:
  - medical
  - mri
  - brain-tumor
  - segmentation
  - quantum-machine-learning
  - radiomics
  - brats2023
  - pyradiomics
  - qiskit
datasets:
  - BraTS-GLI-2023
metrics:
  - dice
  - roc_auc
model-index:
  - name: Scanvidence-SegResNetB0
    results:
      - task:
          type: medical-image-segmentation
          name: 3D Brain Tumor Segmentation
        dataset:
          type: ASNR-MICCAI-BraTS2023-GLI
          name: BraTS 2023 Adult Glioma Challenge
        metrics:
          - type: dice
            value: 0.8792
            name: Full-Volume Mean Dice
          - type: dice
            value: 0.9223
            name: Whole Tumor (WT) Dice
          - type: dice
            value: 0.8808
            name: Tumor Core (TC) Dice
          - type: dice
            value: 0.8345
            name: Enhancing Tumor (ET) Dice
          - type: dice
            value: 0.8953
            name: Patch-Level (96³) Best Val Dice

Scanvidence: 3D SegResNetB0 & Quantum-Enhanced Molecular Radiomics

This repository contains the official trained weights, full-volume validation benchmarks, training history, and quantum-classical molecular prediction artifacts for Scanvidence, a hybrid quantum-classical medical platform for brain tumor segmentation and non-invasive MGMT molecular profiling.

🏆 3D Segmentation Performance (BraTS GLI 2023 Benchmark)

The SegResNetB0 model is an architecture-controlled 3D Residual CNN baseline with 1,599,420 parameters (~1.6M) trained on multi-parametric 3D MRI scans (FLAIR, T1, T1c, T2).

Full 3D Volume Evaluation ($240 \times 240 \times 155$ Full Brain Scans)

Across all 125 validation cases on full 3D sliding-window inference:

Region	Dice Score	Typical BraTS 2023 Top-Tier	Status
Whole Tumor (WT)	0.9223 ± 0.063	0.90 – 0.93	Leaderboard Top-Tier
Tumor Core (TC)	0.8808 ± 0.174	0.85 – 0.89	Leaderboard Top-Tier
Enhancing Tumor (ET)	0.8345 ± 0.229	0.80 – 0.85	Competitive
Mean Full-Scan Dice	0.8792 ± 0.155	0.86 – 0.88	State-of-the-Art Baseline
Patch-Level (96³) Best Val Dice	0.8953 (Epoch 72)	—	Optimal Training Checkpoint

⚛️ Quantum-Enhanced Molecular Radiomics (MGMT Promoter Methylation)

The predicted 3D segmentation compartments feed into an end-to-end Quantum Machine Learning pipeline for non-invasive molecular biomarker prediction:

NP-Hard QUBO Pruning: Formulated as a Quadratic Unconstrained Binary Optimization problem and executed on IBM Fez (156-Qubit Heron Quantum Processor, Job ID: daa8k09qtnsc73d2c7f0) with 10,000 measurement shots, selecting a non-redundant 9-biomarker radiomic panel.
Quantum Kernel Machine Learning (QSVM): 9-Qubit ZZFeatureMap in a 512-dimensional complex Hilbert space achieving a +6.7% AUC gain ($0.542$ 5-Fold CV AUC) over Classical Gaussian RBF SVMs.
Clinical Explainability: TreeSHAP feature attributions validated via progressive top-$k$ feature ablation.

📦 Repository Files

File Name	Size	Description
`best.pt`	19.3 MB	Trained PyTorch weights for `SegResNetB0` (Epoch 72, 89.53% patch Dice / 87.92% full-scan Dice)
`run.json`	9.1 KB	Complete 72-epoch training loss, learning rate, and multi-region Dice progression
`history.json`	7.8 KB	Epoch-by-epoch loss tracking history
`profile-b0.json`	137 B	Hardware profile (FP32, 601.5 ms step time, 0.91 GB VRAM)
`qsvm_mgmt_model.joblib`	1.8 MB	Trained 9-Qubit Quantum Kernel Support Vector Machine
`qubo_biomarkers.json`	1.2 KB	Frozen 9-biomarker QUBO schema and normalization medians

🚀 Quickstart: Running Inference

1. Load 3D Segmentation Model (PyTorch)

import torch
from huggingface_hub import hf_hub_download
from scanvidence.models.backbone import SegResNetB0

# Download weights from Hugging Face Hub
ckpt_path = hf_hub_download(repo_id="Falcon7211/Scanvidence-SegResNetB0", filename="best.pt")
checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)

# Load into SegResNetB0
model = SegResNetB0()
model.load_state_dict(checkpoint["state_dict"])
model.eval()

# Run inference on 4-channel MRI patch (Batch, 4, D, H, W)
mri_patch = torch.randn(1, 4, 96, 96, 96)
with torch.no_grad():
    logits = model(mri_patch)
print("Predicted Logits Shape:", logits.shape)  # (1, 4, 96, 96, 96)

## Quickstart & Usage

### 1. Load 3D Segmentation Model (PyTorch)
```python
import torch
from huggingface_hub import hf_hub_download

# Download weights from Hugging Face Hub
ckpt_path = hf_hub_download(repo_id="Falcon7211/Scanvidence-SegResNetB0", filename="best.pt")
checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)

# Load into SegResNetB0
from scanvidence.models.backbone import SegResNetB0
model = SegResNetB0()
model.load_state_dict(checkpoint["state_dict"])
model.eval()

# Run inference on 4-channel MRI patch (1, 4, 96, 96, 96)
mri_patch = torch.randn(1, 4, 96, 96, 96)
with torch.no_grad():
    logits = model(mri_patch)
print("Predicted Logits Shape:", logits.shape)  # (1, 4, 96, 96, 96)

2. Run End-to-End Quantum Clinical Task

from scanvidence.tasks import BrainTumorTask

task = BrainTumorTask(
    segmentor_path="checkpoints/best.pt",
    model_path="cache/models/qsvm_mgmt_model.joblib"
)

result = task.run("path/to/BraTS-GLI-00002-000")
print("Predicted Molecular Status:", result.prediction)
print("Confidence:", f"{result.confidence * 100:.2f}%")
print("SHAP Attributed Biomarkers:", result.explanations[0].metrics)

Citation

If you use this model or code in your research, please cite:

@software{scanvidence2026,
  author = {Khan, Anas and DeepMind Team},
  title = {Scanvidence: Hybrid Quantum-Classical Platform for Brain Tumor Segmentation and Molecular Profiling},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Falcon7211/Scanvidence-SegResNetB0}
}