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Pulmonary embolisms (PE) are known to be one of the leading causes forcardiac-related mortality. Due to inherent variabilities in how PE manifestsand the cumbersome nature of manual diagnosis, there is growing interest inleveraging AI tools for detecting PE. In this paper, we build a two-stagedetection pipeline that is accurate, computationally efficient, robust tovariations in PE types and kernels used for CT reconstruction, and mostimportantly, does not require dense annotations. Given the challenges inacquiring expert annotations in large-scale datasets, our approach producesstate-of-the-art results with very sparse emboli contours (at 10mm slicespacing), while using models with significantly lower number of parameters. Weachieve AUC scores of 0.94 on the validation set and 0.85 on the test set ofhighly severe PEs. Using a large, real-world dataset characterized by complexPE types and patients from multiple hospitals, we present an elaborateempirical study and provide guidelines for designing highly generalizablepipelines.

Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images