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 the following papers:
@article{kori2020abstracting, title={Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability}, author={Kori, Avinash and Natekar, Parth and Krishnamurthi, Ganapathy and Srinivasan, Balaji}, journal={arXiv preprint arXiv:2008.06457}, year={2020} } @article{natekar2020demystifying, title={Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis}, author={Natekar, Parth and Kori, Avinash and Krishnamurthi, Ganapathy}, journal={Frontiers in Computational Neuroscience}, volume={14}, pages={6}, year={2020}, publisher={Frontiers} } 
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)
