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.

翻译：磁共振成像（MRI）是脑肿瘤分割中常用的技术，对患者评估和治疗规划至关重要。为减少标注过程对专业知识的依赖和人力消耗，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，现有基于CAM的WSSS方法利用神经网络内部信息（如梯度或可训练参数）生成目标定位图，可能导致次优解。为解决这些问题，我们提出置信度诱导的类激活映射（Cfd-CAM），该方法通过目标类别的置信度计算每个特征图的权重。在两个脑肿瘤数据集上的实验表明，在相同监督水平下，Cfd-CAM的性能优于现有最先进方法。总体而言，我们提出的Cfd-CAM方法提高了脑肿瘤分割的精度，并为开发其他医学影像任务中更优的WSSS方法提供了重要启示。