ALL-IDB PATCHES: WHOLE SLIDE IMAGING FOR ACUTE LYMPHOBLASTIC LEUKEMIA DETECTION USING DEEP LEARNING

The detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) is being increasingly performed using Deep Learning models (DL) that analyze each blood sample to detect the presence of lymphoblasts, possible indicators of the disease. However, images included in current databases are either too large or already segmented. In this paper, we introduce ALL-IDB Patches, a novel approach for processing Whole Slide Images (WSI) of ALL to take advantage of all the information available for ALL detection, by generating a larger number of samples and making the images usable by current DL models, without any pre-performed segmentation. To evaluate the attainable classification accuracy, we consider the OrthoALLNet, a Convolutional Neural Network (CNN) obtained by imposing an additional orthogonality constraint on the learned filters. The experimental results confirm the validity of our approach.