README.md

---
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

[![Paper](https://img.shields.io/badge/Paper-IEEE%20TMI%20%2F%20MedIA-blue)](https://github.com)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Model Type](https://img.shields.io/badge/Architecture-ConvNeXt--Tiny%20%2B%20MHA-purple)](https://github.com)
[![AUROC](https://img.shields.io/badge/Official%20MRNet%20AUROC-0.9639-brightgreen)](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}
}
```