论文标题
用MIL-CAM进行弱监督的Minirhizotron图像分割
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM
论文作者
论文摘要
我们提供了一个多个实例学习类激活图（MIL-CAM）方法，用于给定弱图像级标签，用于像素级MinirHizotron图像分割。 Minirhizotron用于原位图像植物根。 Minirhizotron图像通常由土壤组成，其中包含一些长直径的长根对象。对于现有的语义图像分割方法而言，根源被证明是具有挑战性的。除了从弱标签中学习外，我们提出的MIL-CAM方法还会在分析过程中重量重量与土壤像素，以提高由于土壤和根像素之间的严重失衡而导致的性能。所提出的方法的表现优于其他注意力图和多个实例学习方法，用于在Minirhizotron图像中定位根对象。
We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few long and thin root objects of small diameter. The roots prove to be challenging for existing semantic image segmentation methods to discriminate. In addition to learning from weak labels, our proposed MIL-CAM approach re-weights the root versus soil pixels during analysis for improved performance due to the heavy imbalance between soil and root pixels. The proposed approach outperforms other attention map and multiple instance learning methods for localization of root objects in minirhizotron imagery.
