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:仅以图像级别监督的微弱监督语义分解( WSSS) 是处理分解网络需求的一个很有希望的方法, 特别是为了在给定数据集中生成大量像素假面罩。 然而, 多数最先进的图像级 WSS 技术缺乏对图像中嵌入的几何特征的理解, 因为网络无法从图像级别标签中获取任何对象边界信息。 我们在这里将边界定义为分隔对象及其背景或两个不同对象的线条。 为解决这一缺陷, 我们建议了我们的新颖的 LiberCAM 框架, 该框架将最先进的级动画与各种后处理技术相结合, 以便实现精密的更高精密度分解面。 为了实现这一目标, 我们调查了能够用于构建边界图的状态图的状态, 使LiberCAM 能够以更清晰的边界预测对象位置。 我们用我们的方法预测了WSSS, 我们得以实现最高至10%的高级类动画地图和后处理技术, 从而实现精细度高精度的/ 高精确度的图像分解面面 。 为了目前状态的IMIS/ IMIS- b 的在线 系统 的系统 的系统化框架的好处, 。</s>