Automated detection of pulmonary embolism from CT-angiograms using deep learning

Exploring foci of: BMC Medical Imaging • Vol 22 • No 1 Automated detection of pulmonary embolism from CT-angiograms using deep learning March 2022 • Heidi Huhtanen, Mikko Nyman, Tarek Mohsen, Arho Virkki, Antti Karlsson, Jussi Hirvonen Abstract Background The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data. Methods We developed a deep neural network model consisting of two parts: a convolutional neural network architecture called InceptionResNet V2 and a long-short term memory network to process whole CTPA stacks as sequences of slices. Two versions of the model were create… Open Article Page

Artificial Intelligence Deep Learning Convolutional Neural Network Receiver Operating Characteristic Computer Science Pulmonary Embolism Radiology Medicine Machine Learning Open Article

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