  Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor
segmentation, which is critical for evaluating patients and planning treatment.
To make the labeling process less laborious and dependent on expertise,
weakly-supervised semantic segmentation (WSSS) methods using class activation
mapping (CAM) have been proposed. However, current CAM-based WSSS methods
generate the object localization map using internal neural network information,
such as gradient or trainable parameters, which can lead to suboptimal
solutions. To address these issues, we propose the confidence-induced CAM
(Cfd-CAM), which calculates the weight of each feature map by using the
confidence of the target class. Our experiments on two brain tumor datasets
show that Cfd-CAM outperforms existing state-of-the-art methods under the same
level of supervision. Overall, our proposed Cfd-CAM approach improves the
accuracy of brain tumor segmentation and may provide valuable insights for
developing better WSSS methods for other medical imaging tasks.
