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We propose a novel method that combines a convolutional neural network (CNN)with a long short-term memory (LSTM) mechanism for accurate prediction ofintracranial hemorrhage on computed tomography (CT) scans. The CNN plays therole of a slice-wise feature extractor while the LSTM is responsible forlinking the features across slices. The whole architecture is trainedend-to-end with input being an RGB-like image formed by stacking 3 differentviewing windows of a single slice. We validate the method on the recent RSNAIntracranial Hemorrhage Detection challenge and on the CQ500 dataset. For theRSNA challenge, our best single model achieves a weighted log loss of 0.0522 onthe leaderboard, which is comparable to the top 3% performances, almost all ofwhich make use of ensemble learning. Importantly, our method generalizes verywell: the model trained on the RSNA dataset significantly outperforms the 2Dmodel, which does not take into account the relationship between slices, onCQ500. Our codes and models is publicly avaiable athttps://github.com/VinBDI-MedicalImagingTeam/midl2020-cnnlstm-ich.

A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans