Weakly Supervised Semantic Segmentation (WSSS) relying only on 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 are proposing our novel ReFit 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 segmentation network that can be used to construct a boundary map, which enables ReFit to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that our results are reproducible, and accessible online at https://github.com/bharathprabakaran/ReFit.

翻译：仅依赖图像级标注的弱监督语义分割（WSSS）是一种极具前景的方法，可满足分割网络的需求，尤其在需为给定数据集生成大量像素级掩膜时。然而，当前最先进的图像级WSSS技术大多缺乏对图像中几何特征的理解，因为网络无法仅通过图像级标签获取目标边界信息（本文中边界定义为目标与背景或两个不同目标之间的分隔线）。为解决这一缺陷，我们提出创新的ReFit框架，该框架结合最先进的类激活图与多种后处理技术，以实现细粒度、高精度的分割掩膜。为此，我们研究了一种可用于构建边界图的最先进无监督分割网络，使得ReFit能够以更清晰的边界预测目标位置。将我们的方法应用于WSSS预测后，在医学图像领域，我们相较于当前最先进的WSSS方法实现了高达10%的性能提升。该框架为开源项目，确保结果可复现，可通过https://github.com/bharathprabakaran/ReFit 在线访问。