Overview / Usage

Ablation Usage

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)

Dissection Usage

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)

Results

GradCAM Usage

dice = flow.cam(model, img, gt, nclasses = nclasses, save_path = save_path, layer_idx = -1, threshol = 0.5, modifier = 'guided')

Results

Activation Maximization Usage

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

Uncertainty Usage

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)

Results

Radiomics Usage

feat_extractor = radfeatures.ExtractRadiomicFeatures(image, mask, save_path = pth) df = feat_extractor.all_features()

Methodology / Approach

Defined Pipeline

Technologies Used

Features

BioExp supports the following interpretability methods:

• Model Dissection Analysis
• Model Ablation Analysis
• Model Uncertainty Analysis
  • Epistemic Uncertainty using Bayesian Dropout
  • Aleatoric Uncertainty using Test Time Augmentation
• GradCAM
• Activation Maximization

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Repository

https://github.com/koriavinash1/BioExp