论文标题
CA-NET：可解释的医学图像细分的全面注意卷积神经网络
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
论文作者
论文摘要
准确的医学图像分割对于疾病的诊断和治疗计划至关重要。卷积神经网络（CNN）已经实现了自动医疗图像分割的最先进性能。但是，他们仍然受到复杂条件的挑战，即分割目标具有较大的位置，形状和规模的变化，并且现有的CNN具有较差的解释性，从而限制了其在临床决策中的应用。在这项工作中，我们在CNN体系结构中广泛使用多种关注点，并提出了全面的基于注意力的CNN（CA-NET），以更准确，可解释的医学图像细分，以同时了解最重要的空间位置，频道和尺度。特别是，我们首先提出一个联合空间注意模块，以使网络更多地关注前景区域。然后，提出了一个新型的通道注意模块，以适应性重新校准通道特征响应并突出显示最相关的特征通道。此外，我们提出了一个刻度注意模块，该模块暗示着强调多个尺度之间最显着的特征图，以便CNN适应对象的大小。关于皮肤病变细分的广泛实验和胎儿MRI的多级分割，我们提议的CA-NET显着提高了皮肤病变的平均分割骰子分数从87.77％到92.08％，皮肤病变的平均分段得分从84.79％到84.79％到87.08％的平均分段评分，而胎盘和93.20％的平均分段分段得分和93.20％的平均分段分段评分。与最先进的DeepLabV3+相比，它将模型尺寸降低到较小甚至更好的准确性约15倍。此外，通过可视化注意力映射图，它的解释性比现有网络更高。我们的代码可在https://github.com/hilab-git/ca-net上找到
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net
