Behboodi Bahareh, Fortin Maryse, Belasso Clyde J, Brooks Rupert, Rivaz Hassan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2117-2120. doi: 10.1109/EMBC44109.2020.9175846.
Automatic and accurate segmentation of medical images is an important task due to the direct impact of this procedure on both disease diagnosis and treatment. Segmentation of ultrasound (US) imaging is particularly challenging due to the presence of speckle noise. Recent deep learning approaches have demonstrated remarkable findings in image segmentation tasks, including segmentation of US images. However, many of the newly proposed structures are either task specific and suffer from poor generalization, or are computationally expensive. In this paper, we show that the receptive field plays a more significant role in the network's performance compared to the network's depth or the number of parameters. We further show that by controlling the size of the receptive field, a deep network can instead be replaced by a shallow network.
由于医学图像自动精确分割对疾病诊断和治疗都有直接影响，因此它是一项重要任务。由于存在斑点噪声，超声（US）成像的分割尤其具有挑战性。最近的深度学习方法在图像分割任务中取得了显著成果，包括超声图像分割。然而，许多新提出的结构要么是特定于任务的，泛化能力较差，要么计算成本很高。在本文中，我们表明，与网络深度或参数数量相比，感受野在网络性能中起着更重要的作用。我们进一步表明，通过控制感受野的大小，深度网络可以被浅网络取代。
