Abhishek-Shaw-Kolkata

import import_ipynb
from utils import mask2rle,combined_loss,dice_coef
import cv2
import tensorflow as tf
from tensorflow import keras
from tqdm import tqdm
def predict_results(test_files_png):
    '''
         Given a list of chest Xray image files it generates prediction results for Pneumothorax disease
        Args:

Abhishek-Shaw-Kolkata

%env SM_FRAMEWORK=tf.keras
import segmentation_models as sm
import tensorflow as tf
from tensorflow import keras
def get_segmentaion_model(name= 'Uefficientnetb4', BACKBONE = 'efficientnetb4',ENCODER_WEIGHTS = 'imagenet'):
    '''
        Creates segmentaion model object and compiles it with Adam optimizer and combined_loss function
        Args:
            name : Name of model 
            BACKBONE : BACKBONE model name to be used as encider part

Abhishek-Shaw-Kolkata

import tensorflow as tf
from tensorflow.keras import Model
dense_net_121 = tf.keras.applications.DenseNet121(input_shape=[256,256,3],include_top=False,pooling='avg')
base_model_output = tf.keras.layers.Dense(units=14,activation='relu')(dense_net_121.output)
base_model = Model(inputs = dense_net_121.input,outputs=base_model_output)
base_model.load_weights('brucechou1983_CheXNet_Keras_0.3.0_weights.h5')
output_layer = tf.keras.layers.Dense(1,activation='sigmoid')(base_model.layers[-2].output)
model = Model(inputs=base_model.inputs, outputs=output_layer)
model1=tf.keras.layers.UpSampling2D((2,2))(model.layers[-3].output)
model1=tf.keras.layers.concatenate([model1,model.get_layer('pool4_conv').output])

Abhishek-Shaw-Kolkata

import tensorflow as tf
from tensorflow import keras
def get_CheXNet_model(HEIGHT,WIDTH,N_CHANNELS):
    base_model  = keras.applications.DenseNet121(
                                    weights=None,
                                    include_top=False,
                                    input_shape=(HEIGHT,WIDTH,N_CHANNELS), pooling="avg"
                                    )
    predictions = keras.layers.Dense(14, activation='sigmoid', name='predictions')(base_model.output)
    base_model = keras.Model(inputs=base_model.input, outputs=predictions,name='CheXNet')

Abhishek-Shaw-Kolkata

def conv_block(input, num_filters):
    x = Conv2D(num_filters, 3, padding="same")(input)
    x = BatchNormalization()(x)
    x = Activation("relu")(x)

    x = Conv2D(num_filters, 3, padding="same")(x)
    x = BatchNormalization()(x)
    x = Activation("relu")(x)

    return x

Abhishek-Shaw-Kolkata

def extract_metadata_from_images(file_path):
    '''
        Extracts metadata present in DICOM file
        Args: 
            file :  DICOM file path
        Returns:
                a dictionary containing important metadata 
    '''

    dataset = pydicom.dcmread(file_path)