Update README.md

README.md

@@ -2,7 +2,6 @@
 tags:
 - semantic segmentation
 - brain tumor segmentation
-license: mit
 library_name: TensorFlow
 datasets:
 - https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1
@@ -10,4 +9,19 @@ metrics:
 - Dice Coefficient
 - Tversky Loss
 pipeline_tag: image-segmentation
----
+---
+# 3D Attention-based UNet for Multi-modal Brain Tumor Segmentation
+
+## Model Description
+This model uses the UNet architecture, which employs a contracting path to down-sample image dimensions and an expanding path to up-sample while retaining spatial information through skip connections. 
+3D attention gates are introduced to generate 3D channel and spatial attention by utilizing 3D inter-channel and inter-spatial feature relationships.
+The input is a combined scan of 3 modalities (T1CE, T2 and T2-FLAIR) with the dimensions: 3 x L x W x no. of slices. 
+The model attained a Dice Coefficient score of 0.9562 and a Tversky Loss of 0.0438
+
+## Dataset Description
+The BRaTs (Brain Tumor Segmentation) 2021 Dataset, consisting of 1400 multi-parametric MRI (mpMRI) scans with expert neuro-radiologists' ground truth annotations, was used for this project. 
+The dataset provides mpMRI scans in NIfTI format and includes native (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, along with manually annotated GD-enhancing tumor, peritumoral edematous/invaded tissue, necrotic tumor core, and normal tissue.
+Checkout the dataset [here](https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1).
+
+## Model Notebook
+Find the notebook containing the model code on [my Github](https://github.com/MaryannGitonga/Brain-Tumor-Segmentation-Using-3D-Attention-Based-UNet).