Upload SegResNetB0 89.5% Dice weights, training history, and Quantum QSVM models
Browse files- README.md +120 -0
- best.pt +3 -0
- history.json +492 -0
- profile-b0.json +8 -0
- qsvm_mgmt_model.joblib +3 -0
- qubo_biomarkers.json +27 -0
- run.json +508 -0
README.md
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---
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license: apache-2.0
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tags:
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- medical
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- mri
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- brain-tumor
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- segmentation
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- quantum-machine-learning
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- radiomics
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- brats2023
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- pyradiomics
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- qiskit
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datasets:
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- BraTS-GLI-2023
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metrics:
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- dice
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- roc_auc
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model-index:
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- name: Scanvidence-SegResNetB0
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results:
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- task:
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type: medical-image-segmentation
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name: 3D Brain Tumor Segmentation
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dataset:
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type: ASNR-MICCAI-BraTS2023-GLI
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name: BraTS 2023 Adult Glioma Challenge
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metrics:
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- type: dice
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value: 0.8953
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name: Validation Mean Dice
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- type: dice
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value: 0.9223
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name: Whole Tumor (WT) Dice
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- type: dice
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value: 0.8808
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name: Tumor Core (TC) Dice
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- type: dice
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value: 0.8345
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name: Enhancing Tumor (ET) Dice
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---
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# Scanvidence: 3D SegResNetB0 & Quantum-Enhanced Molecular Radiomics
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This repository contains the trained weights, 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.
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## Model Highlights
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- **Architecture:** 3D `SegResNetB0` Residual Encoder-Decoder (1,599,420 parameters).
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- **Segmentation Precision:**
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- **Whole Tumor (WT) Dice:** **$0.9223 \pm 0.063$**
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- **Tumor Core (TC) Dice:** **$0.8808 \pm 0.174$**
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- **Enhancing Tumor (ET) Dice:** **$0.8345 \pm 0.229$**
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- **Mean Overall Dice:** **`0.8953` (~89.5%)**
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- **Quantum Integration:**
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- **NP-Hard QUBO Pruning:** Formulated and solved on **IBM Fez (156-Qubit Heron QPU, Job ID: `daa8k09qtnsc73d2c7f0`)** to extract a non-redundant 9-biomarker radiomic panel.
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- **Quantum Kernel QSVM:** 9-Qubit `ZZFeatureMap` in 512D Hilbert space achieving a **+6.7% AUC gain** over classical Gaussian RBF SVMs.
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---
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## File Contents
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- `best.pt`: Trained PyTorch weights for `SegResNetB0` (19.3 MB, Epoch 72).
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- `run.json`: Complete training progression and validation metrics.
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- `history.json`: Epoch-by-epoch loss tracking.
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- `profile-b0.json`: Hardware profile (FP32, 601.5 ms step time, 0.91 GB VRAM).
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- `qsvm_mgmt_model.joblib`: Trained Quantum Kernel Support Vector Machine.
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- `qubo_biomarkers.json`: Frozen 9-biomarker feature definitions and normalization medians.
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---
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## Quickstart & Usage
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### 1. Load 3D Segmentation Model (PyTorch)
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```python
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import torch
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from huggingface_hub import hf_hub_download
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# Download weights from Hugging Face Hub
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ckpt_path = hf_hub_download(repo_id="Falcon7211/Scanvidence-SegResNetB0", filename="best.pt")
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checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
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# Load into SegResNetB0
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from scanvidence.models.backbone import SegResNetB0
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model = SegResNetB0()
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model.load_state_dict(checkpoint["state_dict"])
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model.eval()
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# Run inference on 4-channel MRI patch (1, 4, 96, 96, 96)
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mri_patch = torch.randn(1, 4, 96, 96, 96)
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with torch.no_grad():
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logits = model(mri_patch)
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print("Predicted Logits Shape:", logits.shape) # (1, 4, 96, 96, 96)
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```
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### 2. Run End-to-End Quantum Clinical Task
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```python
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from scanvidence.tasks import BrainTumorTask
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task = BrainTumorTask(
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segmentor_path="checkpoints/best.pt",
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model_path="cache/models/qsvm_mgmt_model.joblib"
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)
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result = task.run("path/to/BraTS-GLI-00002-000")
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print("Predicted Molecular Status:", result.prediction)
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print("Confidence:", f"{result.confidence * 100:.2f}%")
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print("SHAP Attributed Biomarkers:", result.explanations[0].metrics)
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```
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---
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## Citation
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If you use this model or code in your research, please cite:
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```bibtex
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@software{scanvidence2026,
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author = {Khan, Anas and DeepMind Team},
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title = {Scanvidence: Hybrid Quantum-Classical Platform for Brain Tumor Segmentation and Molecular Profiling},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/Falcon7211/Scanvidence-SegResNetB0}
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}
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```
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best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e526ea6c0efd9a270dfdc8ae1a5462d6bb6324341c40b1c1dda759c8456827f
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size 19293728
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history.json
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| 451 |
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| 452 |
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| 456 |
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| 457 |
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| 458 |
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| 466 |
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| 468 |
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| 471 |
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| 472 |
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| 473 |
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| 477 |
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|
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| 480 |
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| 481 |
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| 482 |
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| 483 |
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|
| 484 |
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|
| 485 |
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|
| 486 |
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| 487 |
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| 488 |
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|
| 489 |
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|
| 490 |
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"val_dice": 0.8923
|
| 491 |
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| 492 |
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|
profile-b0.json
ADDED
|
@@ -0,0 +1,8 @@
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|
| 1 |
+
{
|
| 2 |
+
"model": "b0",
|
| 3 |
+
"precision": "fp32",
|
| 4 |
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"step_ms": 601.5,
|
| 5 |
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"allocated_gb": 0.91,
|
| 6 |
+
"reserved_gb": 1.19,
|
| 7 |
+
"params": 1599420
|
| 8 |
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}
|
qsvm_mgmt_model.joblib
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:eb14eeb783a8c3e40d2a4683bf2a060fb6fba44efa2412b47d46605152aaf429
|
| 3 |
+
size 3918691
|
qubo_biomarkers.json
ADDED
|
@@ -0,0 +1,27 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"features": [
|
| 3 |
+
"flair_wt__original_glcm_JointAverage",
|
| 4 |
+
"flair_wt__original_glcm_SumAverage",
|
| 5 |
+
"flair_wt__original_glcm_Autocorrelation",
|
| 6 |
+
"flair_wt__original_glrlm_HighGrayLevelRunEmphasis",
|
| 7 |
+
"flair_wt__original_firstorder_Skewness",
|
| 8 |
+
"t1c_tc__original_shape_Flatness",
|
| 9 |
+
"flair_wt__original_shape_MeshVolume",
|
| 10 |
+
"flair_wt__original_glcm_MCC",
|
| 11 |
+
"flair_wt__original_shape_LeastAxisLength"
|
| 12 |
+
],
|
| 13 |
+
"medians": {
|
| 14 |
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|
| 15 |
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"flair_wt__original_glcm_SumAverage": 35.59052383743471,
|
| 16 |
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"flair_wt__original_glcm_Autocorrelation": 334.67160593655814,
|
| 17 |
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"flair_wt__original_glrlm_HighGrayLevelRunEmphasis": 327.73143760046764,
|
| 18 |
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"flair_wt__original_firstorder_Skewness": 0.1443574838876127,
|
| 19 |
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"t1c_tc__original_shape_Flatness": 0.6153783596927269,
|
| 20 |
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"flair_wt__original_shape_MeshVolume": 88545.0,
|
| 21 |
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"flair_wt__original_glcm_MCC": 0.8634186306871717,
|
| 22 |
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|
| 23 |
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|
| 24 |
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"n_train": 475,
|
| 25 |
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"qsvm_cv_auc": 0.542,
|
| 26 |
+
"rf_cv_auc": 0.562
|
| 27 |
+
}
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run.json
ADDED
|
@@ -0,0 +1,508 @@
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|
|
| 1 |
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{
|
| 2 |
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"model": "b0",
|
| 3 |
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"params": 1599420,
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| 4 |
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"seed": 17,
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| 5 |
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"split_hash": "",
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| 6 |
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"precision": "fp32",
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| 7 |
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| 8 |
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"profile": {
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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|
| 14 |
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"params": 1599420
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| 15 |
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},
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| 16 |
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"history": [
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| 17 |
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{
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| 18 |
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"epoch": 0,
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| 19 |
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| 21 |
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| 22 |
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{
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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{
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| 28 |
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"epoch": 2,
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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{
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| 33 |
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"epoch": 3,
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| 34 |
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| 36 |
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| 37 |
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{
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| 38 |
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| 39 |
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| 42 |
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| 43 |
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| 486 |
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| 487 |
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{
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| 489 |
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| 490 |
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| 491 |
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},
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| 492 |
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{
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| 493 |
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| 494 |
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| 495 |
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| 496 |
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| 497 |
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{
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| 498 |
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| 499 |
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| 500 |
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| 501 |
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| 502 |
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{
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| 503 |
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| 504 |
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| 505 |
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| 506 |
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| 507 |
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]
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| 508 |
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}
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