  Pulmonary embolisms (PE) are known to be one of the leading causes for
cardiac-related mortality. Due to inherent variabilities in how PE manifests
and the cumbersome nature of manual diagnosis, there is growing interest in
leveraging AI tools for detecting PE. In this paper, we build a two-stage
detection pipeline that is accurate, computationally efficient, robust to
variations in PE types and kernels used for CT reconstruction, and most
importantly, does not require dense annotations. Given the challenges in
acquiring expert annotations in large-scale datasets, our approach produces
state-of-the-art results with very sparse emboli contours (at 10mm slice
spacing), while using models with significantly lower number of parameters. We
achieve AUC scores of 0.94 on the validation set and 0.85 on the test set of
highly severe PEs. Using a large, real-world dataset characterized by complex
PE types and patients from multiple hospitals, we present an elaborate
empirical study and provide guidelines for designing highly generalizable
pipelines.
