| --- |
| language: |
| - en |
| license: mit |
| library_name: timm |
| pipeline_tag: image-classification |
| tags: |
| - medical-imaging |
| - knee-mri |
| - acl-tear-detection |
| - deep-learning |
| - convnext |
| - self-attention |
| - masked-slice-modeling |
| - radiology |
| - orthopedics |
| datasets: |
| - stanford-mrnet |
| metrics: |
| - roc_auc |
| - accuracy |
| - f1 |
| model-index: |
| - name: ACL-LKNet |
| results: |
| - task: |
| type: image-classification |
| name: Knee MRI ACL Tear Detection |
| dataset: |
| type: stanford-mrnet |
| name: Stanford MRNet Locked Test Cohort (N=120) |
| metrics: |
| - type: roc_auc |
| value: 0.9639 |
| name: AUROC |
| - type: precision_recall_auc |
| value: 0.9293 |
| name: AUPRC |
| - type: accuracy |
| value: 0.8167 |
| name: Accuracy |
| - type: specificity |
| value: 0.9394 |
| name: Specificity |
| - type: sensitivity |
| value: 0.6667 |
| name: Sensitivity |
| - type: f1 |
| value: 0.7660 |
| name: F1 Score |
| --- |
| |
| # ACL-LKNet: Self-Supervised Large-Kernel Network for ACL Tear Detection in Knee MRI |
|
|
| [](https://github.com) |
| [](https://opensource.org/licenses/MIT) |
| [](https://github.com) |
| [](https://github.com) |
|
|
| **ACL-LKNet** is an anatomically grounded deep learning architecture specifically engineered for automated Anterior Cruciate Ligament (ACL) tear detection from tri-planar (Sagittal, Coronal, and Axial) volumetric knee MRI examinations. |
|
|
| Developed as part of a doctoral investigation in computational musculoskeletal radiology, ACL-LKNet combines: |
| 1. **Large-Kernel 2D Backbone (ConvNeXt-Tiny)**: Large $7 \times 7$ depthwise convolutions capturing the complete oblique trajectory of intra-articular ligaments. |
| 2. **Masked Slice Modeling (MSM)**: Volumetric self-supervised pretext reconstruction across anisotropic slice stacks. |
| 3. **Parametric Slice Attention**: Dynamic slice sequence pooling that outputs explicit, interpretable slice attention weights $\alpha_{p,s}$. |
| 4. **Tri-Planar Cross-Attention Fusion**: 2-head self-attention operating over learned plane positional embeddings ($e_{\text{sag}}, e_{\text{cor}}, e_{\text{axi}}$). |
| 5. **Strict Anatomical Invariants**: No horizontal/vertical flipping during training to preserve internal knee joint chirality and oblique ACL orientation. |
|
|
| --- |
|
|
| ## Benchmark Performance on Stanford MRNet |
|
|
| Evaluated on the locked, official **Stanford MRNet benchmark test set** ($N=120$ examinations, 54 tears, 66 controls) with empirical 95% bootstrap confidence intervals ($N=1{,}000$ iterations): |
|
|
| | Diagnostic Metric | ACL-LKNet (5-Fold Ensemble) | 95% Bootstrap CI | Stanford MRNet Baseline (Bien et al., 2018) | Absolute $\Delta$ Gain | |
| | :--- | :---: | :---: | :---: | :---: | |
| | **AUROC** | **0.9639** | **[0.9277, 0.9919]** | 0.9370 | **+0.0269** | |
| | **AUPRC** | **0.9293** | **[0.8492, 0.9889]** | -- | -- | |
| | **Accuracy** | **81.67%** | **[75.00%, 88.33%]** | 82.50% | $-0.0083$ | |
| | **Specificity** | **93.94%** | **[87.69%, 98.59%]** | 96.80% | $-0.0286$ | |
| | **Sensitivity** | **66.67%** | **[53.22%, 79.25%]** | 75.90% | $-0.0923$ | |
| | **F1-Score** | **0.7660** | **[0.6585, 0.8519]** | -- | -- | |
| | **Brier Score** | **0.1184** | **[0.0891, 0.1520]** | -- | Well-Calibrated | |
|
|
| > **Statistical Significance (RQ1)**: Paired DeLong test comparing ConvNeXt-Tiny against ResNet-18 yields **$z = 3.864, p = 0.000104$** ($p < 0.001$), establishing the statistical superiority of large receptive fields for elongated ligament structures. |
|
|
| --- |
|
|
| ## Quickstart: Python Inference via Hugging Face Hub |
|
|
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
| |
| # 1. Download model weights from Hugging Face Hub |
| checkpoint_path = hf_hub_download( |
| repo_id="shareefch1413/ACL-LKNet", |
| filename="finetune_best.pt" |
| ) |
| |
| # 2. Instantiate model architecture |
| from src.config import Config |
| from src.models.acl_lknet import create_model_from_config |
| |
| config = Config(backbone_name="convnext_tiny") |
| model = create_model_from_config(config) |
| |
| state_dict = torch.load(checkpoint_path, map_location="cpu") |
| model.load_state_dict(state_dict["ema_state_dict"] if "ema_state_dict" in state_dict else state_dict["model_state_dict"]) |
| model.eval() |
| |
| # 3. Predict on tri-planar MRI volume (Sagittal, Coronal, Axial) |
| # Each volume tensor is shape: (1, 24, 3, 224, 224) |
| dummy_exam = { |
| "sagittal": torch.randn(1, 24, 3, 224, 224), |
| "coronal": torch.randn(1, 24, 3, 224, 224), |
| "axial": torch.randn(1, 24, 3, 224, 224) |
| } |
| |
| with torch.no_grad(): |
| output = model(dummy_exam) |
| tear_probability = torch.sigmoid(output["logits"]).item() |
| |
| print(f"Predicted ACL Tear Probability: {tear_probability * 100:.2f}%") |
| ``` |
|
|
| --- |
|
|
| ## Clinical Interpretability: Grad-CAM++ and Slice Attention |
|
|
| * **Parametric Slice Attention Profiles**: Learns autonomous focus on central intercondylar notch slices (11--15/24) where the ACL is anatomically situated without requiring slice-level bounding box supervision. |
| * **High-Resolution Grad-CAM++**: Hooks into ConvNeXt-Tiny Stage 2 ($14 \times 14$ feature map) to generate intra-articular gradient heatmaps localized to the femoral footprint and midsubstance tear site. |
| * **Decision Curve Analysis (DCA)**: Demonstrates superior clinical net benefit over "treat all" and "treat none" policies across all relevant surgical intervention thresholds ($p_t \in [0.10, 0.75]$). |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @article{acl_lknet2026, |
| title={ACL-LKNet: Anatomically Constrained Large-Kernel Network with Multi-Plane Self-Attention for Volumetric ACL Tear Detection in Knee MRI}, |
| author={PhD Candidate in Biomedical Engineering and Computational Medicine}, |
| journal={IEEE Transactions on Medical Imaging (Preprint / PhD Dissertation Protocol)}, |
| year={2026} |
| } |
| ``` |
| |
