查看文章

The detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is being increasingly performed with the help of Computer Aided Diagnosis (CAD) systems based on Deep Learning (DL), that support the pathologists in performing their decision by analyzing the blood samples to determine the presence of lymphoblasts. When using DL, the limited dimensionality of ALL databases favors the use of transfer learning techniques to increase the accuracy in the detection, by considering Convolutional Neural Networks (CNN) pretrained on the general purpose ImageNet database. However, no method in the literature has yet considered the use of CNNs pretrained on histopathology databases to perform transfer learning for ALL detection. In fact, the majority of histopathology databases in the literature has either a small number of samples or limited ground truth labeling possibilities (e.g., only two possible classes …