It is a great pleasure and honor for everyone in Gatorsense that one of our labmates has achieved his goal. Congratulations to Dr. Guohao Yu for graduating with his Ph.D.! Guohao’s dissertation is titled “Weakly Supervised Image Segmentation with Multiple Instance Learning Neural Network.” Guohao does research on weakly supervised image segmentation. His research focuses on using image-level weak supervision to do semantic segmentation. He proposed the multiple instance learning U-Net (MILUnet) and the multiple instance learning class activation map (MILCAM) algorithms. The MILUnet and MILCAM algorithms are used to address weakly supervised image segmentation problems when the image-level weak supervision satisfies the multiple instance learning constraints. The multiple instance learning constraints require: (1) an image contains at least one pixel from the target class if the image is given the target class label; (2) an image contains no pixels from the target class if the image is given the non target class label. He applied the MILUnet and MILCAM algorithms to segment minirhizotron images. Minirhizotron images are of great interests in dynamically studying root systems. The MILUnet and MILCAM algorithms achieve good results on segment root objects from minirhizotron images.
Read about more Guohao’s research:
G. Yu, A. Zare, H. Sheng, R. Malamala, J. Reyes-Cabrera, F. B. Fritschi and T. E. Juenger, “Root Identiﬁcation in Minirhizotron Imagery with Multiple Instance Learning,” in Machine Vision and Applications, vol. 31, 2020.
G. Yu, A. Zare, W. Xu, R. Matamala, J. Reyes-Cabrera, F.B. Fritschi and T.E. Juenger, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM” in the 16th European Conference on Computer Vision (ECCV) Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020), 2020.
W. Xu, G. Yu, A. Zare, B.Zurweller, D.Rowlan, J.Reyes-Cabrera, F. B. Frischi , R. Malamala and T. E. Juenger “Overcoming Small Minirhizotron Datasets Using Transfer Leraning” in Computers and Electronics in Agriculture, vol. 175, 2020