UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation
This addresses the challenge of accurate prenatal assessment for fetal development by reducing the need for large fine-annotated datasets, though it is incremental as it builds on existing CAM-based methods.
The paper tackled the problem of incomplete activation regions in weakly-supervised fetal brain segmentation from MRI using image-level labels, and the result was that their proposed method outperformed state-of-the-art methods by generating more accurate activation regions with fewer false positives.
Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potential to achieve this task, it requires a large fine annotated dataset that is difficult to collect. To address this issue, weakly-supervised segmentation methods with image-level labels have gained attention, which are commonly based on class activation maps from a classification network trained with image tags. However, most of these methods suffer from incomplete activation regions, due to the low-resolution localization without detailed boundary cues. To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration. We first propose an Uncertainty-weighted Multi-resolution Class Activation Map (UM-CAM) to generate high-quality pixel-level supervision. Then, we design a Geodesic distance-based Seed Expansion (GSE) method to provide context information for rectifying the ambiguous boundaries of UM-CAM. Extensive experiments on a fetal brain dataset show that our UM-CAM can provide more accurate activation regions with fewer false positive regions than existing CAM variants, and our proposed method outperforms state-of-the-art weakly-supervised methods with image-level labels.