  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.
