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README.md

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-# Ensemble-of-CNN-for-3D-Brain-Segmentation-in-T1-MRI
+# Ensemble of Convolutional Neural Networks for 3D Multi-Class Brain Segmentation in T1 MRI
+
+**Problem Statement**: Fully supervised, multi-class 3D brain segmentation in T1 MRI. 
+
+**Note**: The following approach won 1st place in the **2019 Medical Image Segmentation and Applications: Brain Tissue Segmentation Challenge** at [Universitat de Girona](https://www.udg.edu) scoring **92.2% accuracy (kappa: ___) at test-time**, during the 2018-20 Joint Master of Science in [Medical Imaging and Applications (MaIA)](https://maiamaster.udg.edu) program.  
+
+**Acknowledgments**: DLTK for the TensorFlow.Estimator implementation of [3D U-Net, 3D FCN and DeepMedic](https://github.com/qubvel/efficientnet) model architectures. 
+
+**Data**: *Label 1*: Cerebrospinal Fluid (CSF); *Label 2:* Gray Matter (GM); *Label 3:* White Matter (GM) [15/5/3 : Train/Val/Test Ratio]
+
+
+**Directories**  
+  ● Preprocessing Pipeline for Color Space/Constancy: `scripts/color-io.ipynb`  
+  ● Individual Model Training-Validation Pipeline: `scripts/train-val.ipynb`  
+  ● Ensemble Validation Pipeline: `scripts/ensemble-val.ipynb`  
+  ● Ensemble Inference Pipeline: `scripts/ensemble-test.ipynb`               
+
+## Train/Test-Time Data Augmentation  
+
+![Data Augmentation](reports/images/data_augmentation.png)*Figure 1.  All 5 different types of data augmentation [vertical (b)/horizontal (c) flips, brightness shift (d), saturation (e)/contrast (f) boost) used at train-time to broaden the data representation beyond limited pre-existing samples, and test-time to ensure a full prediction from the classifier that is unaffected by the orientation or lighting conditions of the scan. Predictions from all 6 variations [including the original (a)] are averaged to obtain the final prediction per sample.* 
+
+     
+## Multi-Scale Input  
+
+![Multi-Scale Input](reports/images/multi-scale_io.png)*Figure 2.  Original RGB image (left), center cropped 448 x 448 x 3 image used to train 3 CNN member models and the further center cropped 224 x 224 x 3 image used to train 2 more CNN member models. Each model learns to classify at a different scale, with the hypothesis that the collective ensemble benefits from a multi-scale input.* 
+
+
+## Feature Maps  
+
+![Feature Maps](reports/images/imgnet_efn.png)*Figure 3.  Features maps derived from the output of the second block of expanded convolutional layers in a pre-trained EfficientNet-B6 with ImageNet weights, after passing an input skin lesion image through the network.*  
+
+![Feature Maps](reports/images/imgnetplus_efn.png)*Figure 4.  Features maps derived from the output of the second block of expanded convolutional layers in a finetuned EfficientNet-B6 initialized with ImageNet weights, after passing an input skin lesion image through the network.*   
+
+
+## Experimental Results 
+![Results](reports/images/results.png)*Figure 5.  Validation performance for the collective ensemble and each member model.* 
+
+
+## Gradient Class Activation Maps 
+
+![GradCAM](reports/images/gradcam.png)*Figure 6.  Gradient–Class Activation Maps (Grad-CAM) from finetuned EfficientNet-B6 –using  the gradients of the nevus class flowing into the final convolutional layer, to produce a coarse localization map highlighting important regions in the image for predicting nevus.* 
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+