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: 511 Bytes
d2fe28b | 1 2 3 4 5 | Scenario,Accuracy,Precision,Recall,Specificity,Dice,IoU,HD95,ASSD
Binary Classification (Processed),0.986004278526543,0.3461836987030803,0.9649564156425705,0.9997302921073945,0.5095598516138679,0.3418854773044586,,
Three-class Classification (Processed),0.9946109937584918,0.6029034779921095,0.8342410787285695,0.9987378954133063,0.6999530360371915,0.5384059548377991,,
Statistical Analysis,Dice p=0.0000,Dice t=7.9888,Dice Δ=0.1449,Dice ES=0.4778,IoU p=0.0000,IoU Δ=0.1457,HD95 Δ=3.0926px,ASSD Δ=-1.2769px
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