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Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death.While medical imaging, through computed tomographic pulmonary angiography(CTPA), represents the gold standard for PE diagnosis, it is still susceptibleto misdiagnosis or significant diagnosis delays, which may be fatal forcritical cases. Despite the recently demonstrated power of deep learning tobring a significant boost in performance in a wide range of medical imagingtasks, there are still very few published researches on automatic pulmonaryembolism detection. Herein we introduce a deep learning based approach, whichefficiently combines computer vision and deep neural networks for pulmonaryembolism detection in CTPA. Our method features novel improvements along threeorthogonal axes: 1) automatic detection of anatomical structures; 2) anatomicalaware pretraining, and 3) a dual-hop deep neural net for PE detection. Weobtain state-of-the-art results on the publicly available multicenterlarge-scale RSNA dataset.

Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms