Mixup Multi-Attention Multi-Tasking Model for Early-Stage Leukemia Identification

Recently, several image processing and deep learning techniques have been applied to automate the detection of Acute Lymphoblastic Leukemia cells (ALL). However, most of them have consistently focused on classification mature stage cell images into binary categories of ALL or normal cells. The real impetus of biomedical imaging lies in detecting early-stage cases since early-stage ALL cells have unintuitive global contextual and local spatial features, making their detection non-trivial. To this effect, we propose a novel architecture termed as Mixup Multi-Attention Multi-Task Learning Model (MMA-MTL), which introduces Pointwise Attention Convolution Layers and Local Spatial Attention blocks to capture global and local features simultaneously. We also introduce Rademacher Paired Sampling Mixup to prevent memorization of training data in cases of limited categorical shift. Our proposed method shows competitive performance on the ISBI-2019 CNMC dataset and benchmarks appropriate design choices for future biomedical imaging tasks.