AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
This work addresses the need for more accurate and less labor-intensive brain tumor segmentation in medical imaging, though it appears incremental as it builds on existing CAM methods.
The paper tackled the problem of low-resolution class activation maps in weakly supervised semantic segmentation for MRI brain tumor segmentation by proposing AME-CAM, which extracts and aggregates activation maps from multiple resolutions, resulting in improved prediction accuracy on the BraTS 2021 dataset.
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.