Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images

SIMPLE SUMMARY: Pollen allergy is a highly prevalent disease affecting humans worldwide. Early pollen identification can help allergic individuals to prevent pollinosis. Recently, automatic pollen identification (API) has been shown to play a prominent role in pollen concentration monitoring. Developing an accurate and effective identification system may provide new insights for pollinosis prevention. This paper presents a novel automatic pollen identification method integrating localization tasks and classification tasks, thus perfectly mimicking the observation process from palynologists. The inter-task dependence and intra-task reliability are simultaneously considered in this method to effectively enhance the pollen identification performance. We believe that our study will contribute to enhancing symptom control of pollen allergy and maintaining the life quality of allergic patients. ABSTRACT: Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.