  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.
