Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervised semantic segmentation (WSSS) methods with class activation mapping (CAM) have been proposed. However, existing CAM methods suffer from low resolution due to strided convolution and pooling layers, resulting in inaccurate predictions. In this study, we propose a novel CAM method, Attentive Multiple-Exit CAM (AME-CAM), that extracts activation maps from multiple resolutions to hierarchically aggregate and improve prediction accuracy. We evaluate our method on the BraTS 2021 dataset and show that it outperforms state-of-the-art methods.

翻译：磁共振成像（MRI）常用于脑肿瘤分割，这对患者评估和治疗规划至关重要。为减少标注所需的人力和专业知识，基于类激活映射（CAM）的弱监督语义分割（WSSS）方法已被提出。然而，现有CAM方法因步长卷积和池化层导致分辨率较低，从而引发预测不准确。在本研究中，我们提出一种新型CAM方法——注意力多出口类激活映射（AME-CAM），该方法从多个分辨率提取激活图，通过层次化聚合提升预测精度。我们在BraTS 2021数据集上评估了该方法，结果表明其性能优于现有最先进方法。