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Pulmonary Embolism (PE) is a life-threatening disorder associated with highmortality and morbidity. Prompt diagnosis and immediate initiation oftherapeutic action is important. We explored a deep learning model to detect PEon volumetric contrast-enhanced chest CT scans using a 2-stage trainingstrategy. First, a residual convolutional neural network (ResNet) was trainedusing annotated 2D images. In addition to the classification loss, an attentionloss was added during training to help the network focus attention on PE. Next,a recurrent network was used to scan sequentially through the features providedby the pre-trained ResNet to detect PE. This combination allows the network tobe trained using both a limited and sparse set of pixel-level annotated imagesand a large number of easily obtainable patient-level image-label pairs. Weused 1,670 sparsely annotated studies and more than 10,000 labeled studies inour training. On a test set with 2,160 patient studies, the proposed methodachieved an area under the ROC curve (AUC) of 0.812. The proposed framework isalso able to provide localized attention maps that indicate possible PElesions, which could potentially help radiologists accelerate the diagnosticprocess.

Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study