Automatic Whole Slide Pathology Image Diagnosis Framework via Unit Stochastic Selection and Attention Fusion

Abstract Pathology tissue slides are taken as the gold standard for the diagnosis of most cancer diseases. Automatic pathology slide diagnosis is still a challenging task for researchers because of the high-resolution, significant morphological variation, and ambiguity between malignant and benign regions in whole slide images (WSIs). In this study, we introduce a general framework to automatically diagnose different types of WSIs via unit stochastic selection and attention fusion. For example, a unit can denote a patch in a histopathology slide or a cell in a cytopathology slide. To be specific, we first train a unit-level convolutional neural network (CNN) to perform two tasks: constructing feature extractors for the units and for estimating a unit’s non-benign probability. Then we use our novel stochastic selection algorithm to choose a small subset of units that are most likely to be non-benign, referred to as the Units Of Interest (UOI), as determined by CNN. Next, we use the attention mechanism to fuse the representations of the UOI to form a fixed-length descriptor for the WSI’s diagnosis. We evaluate the proposed framework on three datasets: histological thyroid frozen sections, histological colonoscopy tissue slides, and cytological cervical pap smear slides. The framework achieves diagnosis accuracies higher than 0.8 and AUC values higher than 0.85 in all three applications. Experiments demonstrate the generality and effectiveness of the proposed framework and its potentiality for clinical applications.