Explaining Deep Learning Models which perform various image processing tasks in the medical images and natural images.
- Dissection Analysis
- Ablation Analysis
- Uncertainity Analysis
- Epistemic Uncertainty using Bayesian Dropout
- Aleatoric Uncertainty using Test Time Augmentation
- Activation Maximization
- CAM Analysis
- RCT on input and concept space
- Concept generation clustering analysis
- wts based clustering
- feature based clustering
- Concept Identification
- Dissection based
- Flow based
- Causal Graph
- Inference Methods
- Counterfactuals on Visual Trails
- Counterfactual Generation
- Ante-hoc methods (Meta-Causation)
If you use BioExp, please cite our work:
@article{natekar2019demystifying,
title={Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis},
author={Natekar, Parth and Kori, Avinash and Krishnamurthi, Ganapathy},
journal={arXiv preprint arXiv:1909.01498},
year={2019}
}
Running of the explainability pipeline requires a GPU and several deep learning modules.
- 'pandas'
- 'numpy'
- 'scipy==1.6.0'
- 'matplotlib'
- 'pillow'
- 'simpleITK'
- 'opencv-python'
- 'tensorflow-gpu==1.14'
- 'keras'
- 'keras-vis'
- 'lucid'
The following command will install only the dependencies listed above.
pip install BioExp
from BioExp.spatial import Ablation
A = spatial.Ablation(model = model,
weights_pth = weights_path,
metric = dice_label_coef,
layer_name = layer_name,
test_image = test_image,
gt = gt,
classes = infoclasses,
nclasses = 4)
df = A.ablate_filter(step = 1)
from BioExp.spatial import Dissector
layer_name = 'conv2d_3'
infoclasses = {}
for i in range(1): infoclasses['class_'+str(i)] = (i,)
infoclasses['whole'] = (1,2,3)
dissector = Dissector(model=model,
layer_name = layer_name)
threshold_maps = dissector.get_threshold_maps(dataset_path = data_root_path,
save_path = savepath,
percentile = 85)
dissector.apply_threshold(image, threshold_maps,
nfeatures =9,
save_path = savepath,
ROI = ROI)
dissector.quantify_gt_features(image, gt,
threshold_maps,
nclasses = infoclass,
nfeatures = 9,
save_path = savepath,
save_fmaps = False,
ROI = ROI)
from BioExp.spatial import cam
dice = flow.cam(model, img, gt,
nclasses = nclasses,
save_path = save_path,
layer_idx = -1,
threshol = 0.5,
modifier = 'guided')
from BioExp.concept.feature import Feature_Visualizer
class Load_Model(Model):
model_path = '../../saved_models/model_flair_scaled/model.pb'
image_shape = [None, 1, 240, 240]
image_value_range = (0, 10)
input_name = 'input_1'
E = Feature_Visualizer(Load_Model, savepath = '../results/', regularizer_params={'L1':1e-3, 'rotate':8})
a = E.run(layer = 'conv2d_17', class_ = 'None', channel = 95, transforms=True)
##Activation Results
from BioExp.uncertainty import uncertainty
D = uncertainty(test_image)
# for aleatoric
mean, var = D.aleatoric(model, iterations = 50)
# for epistemic
mean, var = D.epistemic(model, iterations = 50)
# for combined
mean, var = D.combined(model, iterations = 50)
from BioExp.helpers import radfeatures
feat_extractor = radfeatures.ExtractRadiomicFeatures(image, mask, save_path = pth)
df = feat_extractor.all_features()
- Avinash Kori (koriavinash1@gmail.com)
- Parth Natekar (parth@smail.iitm.ac.in)