迁移学习在乳腺癌检测中的应用

介绍

每年，美国有超过 230,000 名乳腺癌患者的确诊取决于癌症是否已经转移。转移检测由病理学家检查大片生物组织进行。这个过程是劳动密集型的并且容易出错。

在今天这个项目中，我们的目标是实现论文《在Gigapixel病理图像上检测癌症转移》arxiv：1703.02442 中提出的多尺度转移分类模型。

数据集

我们使用CAMELYON-16 Multi Giga Pixel数据。从主要数据集中取样了22张有显著肿瘤的胶片。每张胶片都有一个对应的掩码来标注肿瘤区域。我们可以达到9级放大倍数。

每一幅多尺度图像以一致的坐标系统叠放在另一幅图像上，以允许用户访问任意放大倍数的任意图像片段。

我们利用OpenSlide C库及其Python绑定来有效地访问十亿像素大小的压缩文件。

数据准备

作为生成标记训练数据的第一步，我们使用滑动窗口方法在更高的缩放级别上滑动，并创建片段的标记图像，稍后我们将使用这些图像进行训练和测试。

我们使用论文中遵循的方法，定义一个中心窗口，如果中心包含至少1个标记为肿瘤的像素，我们将图像标记为包含肿瘤细胞。

窗口大小和中心大小分别被选择为80和50。最后，为了使我们的训练数据具有多尺度，我们创建了原始图像片段的类似大小的缩小版本。缩小后的版本用于为分类模型提供宏观层次的上下文。我们使用多尺度方法生成数据，并使用两种不同的放大倍数来准备数据集。

代码语言：javascript复制

def generate_images(slide, slide_mask, level_1, level_2, window_size, center_size, stride):
  """
  The function generates training data at two different magifications.
  """

  tumor_image = read_slide(slide, 
                         x=0, 
                         y=0, 
                         level=level_1, 
                         width=tumor.level_dimensions[level_1][0], 
                         height=tumor.level_dimensions[level_1][1])

  tumor_mask_image = read_slide(slide_mask, 
                         x=0, 
                         y=0, 
                         level=level_1, 
                         width=tumor.level_dimensions[level_1][0], 
                         height=tumor.level_dimensions[level_1][1])
  
  tumor_mask_image = tumor_mask_image[:,:,0]
  patch_images = []
  patch_labels = []
  patch_center = []
  patch_coord  = []
  
  count = 0
  tumor_count = 0
  health_count = 1

  for i in range(window_size//2, slide.level_dimensions[level_1][1] - window_size - stride, stride):
    for j in range(window_size//2, slide.level_dimensions[level_1][0] - window_size - stride, stride):
      
      patch = tumor_image[i:i window_size, j:j window_size]
      tumor_mask_patch = tumor_mask_image[i:i window_size, j:j window_size]
      
      tissue_pixels = find_tissue_pixels(patch)
      tissue_pixels = list(tissue_pixels)
      percent_tissue = len(tissue_pixels) / float(patch.shape[0] * patch.shape[0]) * 100

      # If contains a tumor in the center
      if check_centre(tumor_mask_patch, center_size):
        patch_images.append(patch)
        patch_labels.append(1)
        patch_coord.append((j, i))
        patch_center.append((j window_size//2, i window_size//2))
        tumor_count  = 1
        continue
        
      # If healthy keep only if the image patch contains more than 50% tissue
      # and sample to keep the memory size small

      if percent_tissue > 75:
        if (health_count < tumor_count) or (np.random.uniform() > (1 - tumor_count/(health_count tumor_count))):
          patch_images.append(patch)
          patch_labels.append(0)
          patch_coord.append((j, i))
          patch_center.append((j window_size//2, i window_size//2))
          health_count  = 1
        
      count  = 1

  tumor_ids = [id for id in range(len(patch_labels)) if int(patch_labels[id]) == 1]
  normal_ids = [id for id in range(len(patch_labels)) if int(patch_labels[id]) == 0]

  # Randomly Shuffle the images
  np.random.shuffle(tumor_ids)
  np.random.shuffle(normal_ids)
  total_ids = tumor_ids   normal_ids
  np.random.shuffle(total_ids)

  patch_images = [patch_images[idx] for idx in total_ids]
  patch_labels = [patch_labels[idx] for idx in total_ids]
  patch_center = [patch_center[idx] for idx in total_ids]
  patch_coord = [patch_coord[idx] for idx in total_ids]
  tumor_image = read_slide(slide, 
                         x=0, 
                         y=0, 
                         level=level_2, 
                         width=tumor.level_dimensions[level_2][0], 
                         height=tumor.level_dimensions[level_2][1])
  patch_images_2 = []
  for center in patch_center:
      patch = tumor_image[center[1]//2-window_size//2:center[1]//2 window_size//2, center[0]//2-window_size//2:center[0]//2 window_size//2]
      assert patch.shape == (window_size, window_size, 3)
      patch_images_2.append(patch)   
  return patch_images, patch_images_2, patch_labels, patch_coord

数据增广

我们使用了各种数据增强技术来补充我们的训练数据，使我们的模型更健壮。

正交旋转和翻转

我们引入正交增广来引入旋转不变性，因为幻灯片可以在这些方向上检查

随机正交旋转
随机水平和垂直翻转

色微扰

为了使我们的模型对光照和颜色强度具有鲁棒性，我们对颜色进行如下扰动。

亮度最大为64/255
饱和度，最大值为0。25
色相的最大增量为0.04
而最大值是0。75

代码语言：javascript复制

#This function applies random brightness, saturation, hue, and constrast to the training images
# The Max Delta Values have been taken directly from the paper
def data_augmentation(img):
    img = tf.image.random_brightness(img, 64.0/255)
    img = tf.image.random_saturation(img, 0.75, 1)
    img = tf.image.random_hue(img, 0.04)
    img = tf.image.random_contrast(img, 0.50, 1)
    return img
    
#This function applies orthogonal rotation transformations to the training data
def orthogonal_rotations(img):
    return np.rot90(img, np.random.choice([-1, 0, 1, 2]))

# The function maps the pixel values between (-1, 1)
def rescale(image):
    image = image / 255.0
    image = (image - 0.5) * 2
    return image

def preprocess(image):
    image = orthogonal_rotations(image)
    image = data_augmentation(image)
    image = rescale(image)
    return image