Weakly Supervised Semantic Segmentation (WSSS) with only image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in the images since the network cannot derive any object boundary information from just image-level labels. We define a boundary here as the line separating an object and its background, or two different objects. To address this drawback, we propose our novel BoundaryCAM framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques in order to achieve fine-grained higher-accuracy segmentation masks. To achieve this, we investigate a state-of-the-art unsupervised semantic segmentation network that can be used to construct a boundary map, which enables BoundaryCAM to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we were able to achieve up to 10% improvements even to the benefit of the current state-of-the-art WSSS methods for medical imaging. The framework is open-source and accessible online at https://github.com/bharathprabakaran/BoundaryCAM.

翻译：仅依赖图像级标注的弱监督语义分割（WSSS）是一种应对分割网络需求的有前景方法，尤其适用于在给定数据集中生成大量像素级掩膜。然而，当前多数最先进的图像级WSSS技术缺乏对图像中几何特征的理解，因为网络无法仅从图像级标签获取任何物体边界信息。此处我们将边界定义为分隔物体与其背景或两个不同物体的轮廓线。为解决这一缺陷，我们提出新型BoundaryCAM框架，该框架结合最先进的类别激活映射与多种后处理技术，以生成高精度的细粒度分割掩膜。为此，我们研究了一种可用于构建边界图的最先进无监督语义分割网络，使BoundaryCAM能够预测具有更清晰边界的物体位置。将本方法应用于WSSS预测后，我们实现了相较现有最先进医学图像WSSS方法最多10%的性能提升。该框架为开源项目，可通过https://github.com/bharathprabakaran/BoundaryCAM在线获取。