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MACHINE LEARNING

Spotting brain bleeding after sparse training

Nature Biomedical Engineering volume 3, pages 161–162 (2019)Cite this article

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Accurate and explainable detection, via deep learning, of acute intracranial haemorrhage from computed tomography images of the head is achievable with small amounts of data for model training.

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Fig. 1: Activation maps and sets of relevant prediction bases help explain the predictions of a deep-learning algorithm for detecting intracranial haemorrhage in computed-tomography images.

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  1. Google LLC., Mountain View, CA, USA

    Michael C. Muelly & Lily Peng

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  1. Michael C. Muelly
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  2. Lily Peng
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Correspondence to Michael C. Muelly.

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Muelly, M.C., Peng, L. Spotting brain bleeding after sparse training. Nat Biomed Eng 3, 161–162 (2019). https://doi.org/10.1038/s41551-019-0368-5

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