DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection

Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are increasingly considering Deep Learning (DL) due to its high accuracy in several fields, including medical imaging. In most cases, such methods use transfer learning techniques to compensate for the limited availability of labeled data. However, current methods for ALL detection use traditional transfer learning, which requires the models to be fully trained on the source domain, then fine-tuned on the target domain, with the drawback of possibly overfitting the source domain and reducing the generalization capability on the target domain. To overcome this drawback and increase the classification accuracy that can be obtained using transfer learning, in this paper we propose our method named "Deep Learning for Acute Lymphoblastic Leukemia" (DL4ALL), a novel multi-task learning DL model for ALL detection, trained using a cross-dataset transfer learning approach. The method adapts an existing model into a multi-task classification problem, then trains it using transfer learning procedures that consider both source and target databases at the same time, interleaving batches from the two domains even when they are significantly different. The proposed DL4ALL represents the first work in the literature using a multi-task cross-dataset transfer learning procedure for ALL detection. Results on a publicly-available ALL database confirm the validity of our approach, which achieves a higher accuracy in detecting ALL with respect to existing methods, even when not using manual labels for the source domain.