Abstract:
Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR i...Show MoreMetadata
Abstract:
Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.
Published in: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Date of Conference: 20-24 July 2020
Date Added to IEEE Xplore: 27 August 2020
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PubMed ID: 33018280