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）是应对分割网络需求（尤其是为给定数据集生成大量逐像素掩码）的一种有前景的方法。然而，当前最先进的图像级WSSS技术缺乏对图像中几何特征的理解，因为网络无法从仅有的图像级标签中获取物体边界信息。这里我们将边界定义为物体与其背景或两个不同物体之间的分隔线。为解决这一缺陷，我们提出新颖的ReFit框架，该框架结合最先进的类激活映射与多种后处理技术，以实现高精度的细粒度分割掩码。为此，我们研究了一种可构建边界图的最先进无监督分割网络，使得ReFit能够预测具有更清晰边界的物体位置。将我们的方法应用于WSSS预测后，在医学影像领域实现了相较于当前最先进WSSS方法最高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在线获取。