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a639402 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset
import cv2
import os
import pydicom
import einops
class Mammography(Dataset):
def __init__(self, excel_file, data_folder, category, task, height, width, background_crop = True, use_clahe= False, transform=None):
"""
Args:
excel_file (string): Path to the excel file with annotations.
category (string) : 'Classification' for Benign and Malignant. 'Subtypes' for Subtype Classification
transform (callable, optional): Optional transform to be applied
"""
self.mammography = pd.read_csv(excel_file, dtype = str)
self.data_folder = data_folder
self.category = category
self.task = task
self.height = height
self.width = width
self.background_crop = background_crop
self.use_clahe = use_clahe
self.transform = transform
def __len__(self):
return len(self.mammography)
def class_name_to_labels(self, idx):
if self.category == 'No Defects':
class_abnormality = self.mammography.iloc[idx, 5]
if class_abnormality in ['1', 1] : # No Defect = True
labels = 0.0
elif class_abnormality in ['0', 0] : # No Defect = False
labels = 1.0
if self.category == 'Skinfolds': ## Either 2i or 3i
class_abnormality = self.mammography.iloc[idx, 6]
if class_abnormality in ['0', 0 ] :
labels = 0.0
elif class_abnormality in ['1', 1]:
labels = 1.0
if self.category == '2i':
class_abnormality = self.mammography.iloc[idx, 7]
if class_abnormality in ['0', 0 ] :
labels = 0.0
elif class_abnormality in ['1', 1] :
labels = 1.0
if self.category == '3i':
class_abnormality = self.mammography.iloc[idx, 8]
if class_abnormality in ['0', 0 ] :
labels = 0.0
elif class_abnormality in ['1', 1] :
labels = 1.0
if self.category == 'Skinfold_MultiLabel':
class_abnormality_skinfold = self.mammography.iloc[idx, 6]
class_abnormality_defect = self.mammography.iloc[idx, 5]
if class_abnormality_skinfold in ['0', 0 ] and class_abnormality_defect in ['1', 1] :
labels = [0., 0.]
elif class_abnormality_skinfold in ['0', 0] and class_abnormality_defect in ['0', 0 ]:
labels = [0., 1.]
elif class_abnormality_skinfold in ['1', 1] and class_abnormality_defect in ['1', 1 ]:
labels = [1., 0.]
elif class_abnormality_skinfold in ['1', 1] and class_abnormality_defect in ['0', 0 ]:
labels = [1., 1.]
if self.category == 'Skinfold_Defect_MultiLabel':
class_abnormality_2i = self.mammography.iloc[idx, 7]
class_abnormality_3i = self.mammography.iloc[idx, 8]
class_abnormality_defect = self.mammography.iloc[idx, 5]
if class_abnormality_2i in ['0', 0 ] and class_abnormality_3i in ['0', 0 ] and class_abnormality_defect in ['1', 1 ]:
labels = [0., 0., 0.]
elif class_abnormality_2i in ['0', 0] and class_abnormality_3i in ['1', 1 ] and class_abnormality_defect in ['0', 0 ]:
labels = [0., 1., 1.]
elif class_abnormality_2i in ['1', 1] and class_abnormality_3i in ['0', 0 ] and class_abnormality_defect in ['0', 0 ]:
labels = [1., 0., 1.]
elif class_abnormality_2i in ['1', 1] and class_abnormality_3i in ['1', 1 ] and class_abnormality_defect in ['0', 0 ]:
labels = [1., 1., 1.]
if self.category == 'Calc_Mass_Malignant_MultiLabel':
abnormality = self.mammography.iloc[idx, 3]
classification = self.mammography.iloc[idx ,4]
if abnormality == 'calcification' and classification == 'Benign':
labels = [1. , 0., 0.]
elif abnormality == 'both' and classification == 'Benign':
labels = [1. , 1., 0.]
elif abnormality == 'mass' and classification == 'Benign':
labels = [0. , 1., 0.]
elif abnormality == 'calcification' and classification == 'Malignant':
labels = [1. , 0., 1.]
elif abnormality == 'both' and classification == 'Malignant':
labels = [1. , 1., 1.]
elif abnormality == 'mass' and classification == 'Malignant':
labels = [0. , 1., 1.]
return labels
return labels
def detect_nonzero_regions(self, numbers):
regions = []
start = None
for i, num in enumerate(numbers):
if num != 0:
if start is None:
start = i
elif start is not None:
regions.append((start, i-1))
start = None
if start is not None:
regions.append((start, len(numbers)-1))
return regions
def crop_images(self, data):
columns = data.shape[1]
count_all=[]
for i in range(columns):
count_non_zeros = np.count_nonzero(data[:, i][20:-20])
count_all.append(count_non_zeros)
column_indices = self.detect_nonzero_regions(count_all)
differences = []
for index, i in enumerate(column_indices):
difference = i[1]-i[0]
differences.append(difference)
index_max = np.argmax(differences)
val = column_indices[index_max]
if index_max == 0 and len(differences)!=1:
column_indices = list(range(val[1], data.shape[1]))
elif index_max == 0 and len(differences)==1:
zero_columns = np.all(data == 0, axis=0)
column_indices = np.where(zero_columns)[0]
else:
column_indices = list(range(0, val[0]))
data = np.delete(data, column_indices, axis=1)
zero_rows = np.all(data == 0, axis=1)
row_indices = np.where(zero_rows)[0]
data = np.delete(data, row_indices, axis=0)
return data
def image_load(self, idx, column):
img_name = self.mammography.iloc[idx, column]
if self.task == 'classification_dcm':
image_name = os.path.join(self.data_folder, img_name +'.dcm')
image = pydicom.dcmread(image_name)
image_array = image.pixel_array
elif self.task == 'classification_png':
image_name = os.path.join(self.data_folder, img_name + '.png')
image_array = cv2.imread(image_name)
elif self.task == 'classification_jpeg':
#filename = os.path.basename(img_name) ## Check why doesnt it work
filename = img_name.split("\\")[-1]
image_name = os.path.join(self.data_folder, filename)
image_array = cv2.imread(image_name)
if self.task == 'classification_dcm' and self.background_crop:
image_array = self.crop_images(image_array)
image = cv2.resize(image_array, (self.width, self.height))
if self.use_clahe:
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) ## Using standard values; Extent of impact unknown.
image = clahe.apply(image)
image = image * 1.0 / image.max()
image = torch.from_numpy(image)
if self.task == 'classification_dcm':
image = image[None, :, :]
image = einops.repeat(image, 'b h w -> (repeat b) h w', repeat=3)
elif self.task in ['classification_png', 'classification_jpeg']:
image = image.permute(2, 0, 1)
return image, image_name
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
image, image_name = self.image_load(idx, 9)
if self.transform:
image = self.transform(image)
labels = self.class_name_to_labels(idx)
labels = torch.from_numpy(np.array(labels))
if self.category in ['Calc_Mass_Malignant_MultiLabel']:
## For displaying the labels on the Output Image / GradCAM plots
abnormality = self.mammography.iloc[idx, 3]
classification = self.mammography.iloc[idx ,4]
display_label = str(abnormality) + ' , ' + str(classification)
else:
## For displaying the labels on the Output Image / GradCAM plots
two_i = self.mammography.iloc[idx, 7]
three_i = self.mammography.iloc[idx, 8]
defect = self.mammography.iloc[idx, 5]
display_label = '2i:' + str(two_i)+ ' 3i:' + str(three_i) + ' Defect: ' + str(defect)
sample = {"image_name":image_name, "image": image, "label" : labels, "display_label":display_label}
return sample |