Image Segmentation
Flair
Keras
tensorflow
medical-imaging
white-matter-hyperintensities
mri
deep-learning
neurology
multiple-sclerosis
Instructions to use Bawil/wmh_leverage_normal_abnormal_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("Bawil/wmh_leverage_normal_abnormal_segmentation") - Keras
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Bawil/wmh_leverage_normal_abnormal_segmentation") - Notebooks
- Google Colab
- Kaggle
File size: 516 Bytes
d2fe28b | 1 2 3 4 5 6 7 8 9 10 11 12 13 | \begin{table}
\caption{Performance comparison between binary and three-class segmentation approaches}
\label{tab:performance_comparison}
\begin{tabular}{lllllllll}
\toprule
Scenario & Accuracy & Precision & Recall & Specificity & Dice & IoU & HD95 & ASSD \\
\midrule
Binary Classification (Processed) & 0.9860 & 0.3462 & 0.9650 & 0.9997 & 0.5096 & 0.3419 & NaN & NaN \\
Three-class Classification (Processed) & 0.9946 & 0.6029 & 0.8342 & 0.9987 & 0.7000 & 0.5384 & NaN & NaN \\
\bottomrule
\end{tabular}
\end{table}
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