# Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

arXiv: https://arxiv.org/abs/2007.15243 · HTML: https://mlsanity.com/paper/2007.15243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Transformer based multiple instance learning for weakly supervised histopathology image segmentation](https://mlsanity.com/paper/2205.08878/) (2022)
- [PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study](https://mlsanity.com/paper/2201.08002/) (2022)

## Abstract (arXiv)

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.