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ebafcec
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1438900
Update app.py
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app.py
CHANGED
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# In[1]:
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# # Defining the path to Model and Images
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BACKBONE = 'efficientnetb0'
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model1 =
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model2 =
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model3 =
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BACKBONE = 'efficientnetb7'
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model4 =
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model5 =
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preprocess_input =
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model1.load_weights(path_base_model + 'model1.hdf5')
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model2.load_weights(path_base_model + 'model2.hdf5')
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def preprocessing_HE(img_):
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hist, bins =
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cdf = hist.cumsum()
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cdf_m =
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cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
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cdf =
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img_2 = cdf[img_]
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# In[1]:
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import keras
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get_ipython().run_line_magic('env', 'SM_FRAMEWORK=tf.keras')
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import segmentation_models as sm
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import glob
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import matplotlib.pyplot as plt
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from scipy import ndimage
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from scipy.ndimage import label, generate_binary_structure
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import radiomics
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import cv2
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import SimpleITK as sitk
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import six
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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get_ipython().run_line_magic('matplotlib', 'inline')
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from sklearn.feature_selection import SelectKBest
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from sklearn.feature_selection import mutual_info_classif
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.feature_selection import RFE
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from sklearn.metrics import roc_auc_score
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from sklearn.feature_selection import SelectFromModel
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from sklearn.linear_model import Lasso, LogisticRegression
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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from sklearn.svm import LinearSVC
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from sklearn.datasets import load_iris
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from sklearn.feature_selection import SelectFromModel
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from sklearn.feature_selection import mutual_info_classif as MIC
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from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
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from sklearn.feature_selection import SelectKBest, SelectPercentile
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from sklearn.feature_selection import SequentialFeatureSelector
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.svm import SVC
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from sklearn.metrics import f1_score
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from sklearn.model_selection import cross_validate
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.linear_model import Perceptron
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.model_selection import GridSearchCV
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import warnings
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warnings.filterwarnings('ignore')
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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# # Defining the path to Model and Images
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BACKBONE = 'efficientnetb0'
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model1 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
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model2 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
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model3 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
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BACKBONE = 'efficientnetb7'
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model4 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
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model5 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
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preprocess_input = sm.get_preprocessing(BACKBONE)
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model1.load_weights(path_base_model + 'model1.hdf5')
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model2.load_weights(path_base_model + 'model2.hdf5')
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def preprocessing_HE(img_):
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hist, bins = np.histogram(img_.flatten(), 256,[0,256])
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cdf = hist.cumsum()
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cdf_m = np.ma.masked_equal(cdf,0)
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cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
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cdf = np.ma.filled(cdf_m,0).astype('uint8')
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img_2 = cdf[img_]
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