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https://github.com/fitushar/3d-guidedgradcam-for-medical-imaging
This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
https://github.com/fitushar/3d-guidedgradcam-for-medical-imaging
3d-guided-grad-cam 3d-models cnn-visualization cnn-visualization-technique ctci explainable-ai explainable-ai-in-medical-imaging explainable-artificial-intelligence guided-grad-cam medical-imaging nifti tensorflow-examples tensorflow2
Last synced: 8 days ago
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This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
- Host: GitHub
- URL: https://github.com/fitushar/3d-guidedgradcam-for-medical-imaging
- Owner: fitushar
- Created: 2020-07-12T22:35:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-07-12T23:20:57.000Z (over 4 years ago)
- Last Synced: 2024-11-12T17:03:15.811Z (8 days ago)
- Topics: 3d-guided-grad-cam, 3d-models, cnn-visualization, cnn-visualization-technique, ctci, explainable-ai, explainable-ai-in-medical-imaging, explainable-artificial-intelligence, guided-grad-cam, medical-imaging, nifti, tensorflow-examples, tensorflow2
- Language: Python
- Homepage:
- Size: 614 KB
- Stars: 99
- Watchers: 2
- Forks: 7
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 3D-GuidedGradCAM-for-Medical-Imaging
This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
## Files:
```ruby
i) guided_Gradcam3D.py |--> Generate Guided-GradCAM , input and output nifti data
ii) Guided_GradCAM_3D_config.py |--> Configuration file for the Guided-GradCAM, Modify based on your need
iii) Resnet_3D.py |--> Network architecture
iv) deploy_config.py |--> Configuration file for the Network, Modify based on your need
v) loss_funnction_And_matrics.py |--> Loss functions for CNN
```
## How to run
To run and generate Guided-GardCAM all is to need to configure the `Guided_GradCAM_3D_config.py` and `deploy_config.py` based on your requiremnet.
### deploy_config.py-
CNN configuration Change based on your Network or complete replace by your CNN
```ruby
import tensorflow as tf
import math
from loss_funnction_And_matrics import*
###---Number-of-GPU
NUM_OF_GPU=1
DISTRIIBUTED_STRATEGY_GPUS=["gpu:0","gpu:1","gpu:2"]
##Network Configuration
NUMBER_OF_CLASSES=5
INPUT_PATCH_SIZE=(224,160,160, 1)
TRAIN_NUM_RES_UNIT=3
TRAIN_NUM_FILTERS=(16, 32, 64, 128)
TRAIN_STRIDES=((1, 1, 1), (2, 2, 2), (2, 2, 2), (2, 2, 2))
TRAIN_CLASSIFY_ACTICATION=tf.nn.relu6
TRAIN_KERNAL_INITIALIZER=tf.keras.initializers.VarianceScaling(distribution='uniform')
TRAIN_CLASSIFY_LEARNING_RATE =1e-4
TRAIN_CLASSIFY_LOSS=Weighted_BCTL
OPTIMIZER=tf.keras.optimizers.Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE,epsilon=1e-5)
TRAIN_CLASSIFY_METRICS=tf.keras.metrics.AUC()
```
### Guided_GradCAM_3D_config.py
Input Configuration for the Guided-GradCAM
```ruby
MODEL_WEIGHT="Path/of/Model/Weight/XXX.h5"
CLASS_INDEX=1 # Index of the class for which you want to see the Guided-gradcam
INPUT_PATCH_SIZE_SLICE_NUMBER=64 # Input patch slice you want to feed at a time
LAYER_NAME='conv3d_18' # Name of the layer from where you want to get the Guided-GradCAM
NIFTI_PATH="imput/niftidata/path/XXX.nii.gz"
SAVE_PATH="/Output/niftydata/path/ML_Guided_GradCaN_XXXX.nii.gz"
```
## Sample Guided-GardCAM
