Image Classification
timm
English
medical-imaging
knee-mri
acl-tear-detection
deep-learning
convnext
self-attention
masked-slice-modeling
radiology
orthopedics
Eval Results (legacy)
Instructions to use shareefch1413/ACL-LKNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use shareefch1413/ACL-LKNet with timm:
import timm model = timm.create_model("hf-hub:shareefch1413/ACL-LKNet", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 903 Bytes
00801a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | # ACL-LKNet: Anatomically Aware Self-Supervised Large-Kernel Network
# for ACL Tear Detection in Knee MRI
from .config import Config
from .dataset import MRNetDataset, MRNetSSLDataset, create_dataloaders, get_stratified_folds
from .evaluate import (
compute_metrics,
compute_metrics_with_ci,
find_optimal_thresholds,
generate_threshold_sweep,
plot_threshold_curves,
plot_confusion_matrices_dual,
evaluate_slice_explainability,
evaluate_perturbation_faithfulness,
evaluate_slice_localization,
evaluate_msm_reconstruction,
evaluate_scanner_perturbation_robustness,
delong_test,
mcnemar_test,
full_evaluation,
)
try:
from .models.acl_lknet import ACLLKNet, create_model_from_config
from .models.msm import MaskedSliceModeling
from .train import train_supervised, pretrain_msm, train_5fold_cross_validation
except ImportError:
pass
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