Parasitic infections are one of the leading causes of deaths and other ailments worldwide. Detecting such infections using traditional diagnostic procedures requires experienced medical technologists together with a significant amount of time and effort. An automated procedure with the ability to accurately detect parasitic diseases can greatly accelerate the process. This work proposes a deep learning-based object detection for parasitic egg detection and classification. We show that multitask learning via pseudo-mask generation improves the single model performance. Moreover, we show that a combination of multitask learning, pseudo-label generation, and ensembling model predictions can accurately detect parasitic egg cells. Continuous training via pseudo-label generation and ensemble predictions improves the accuracy of single-model detection. Our final model achieved a mean precision score (mAP) of 0.956 on a validation set of 1,650 images. Our best model obtained mIoU and mF1 scores of 0.934 and 0.988 respectively. We discuss its technical implementation in this paper.

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