bibtext: @InProceedings{ Zho_Weaklysupervised_MICCAI2024,
                   author = { Zhong, Yuan and Tang, Chenhui and Yang, Yumeng and Qi, Ruoxi and Zhou, Kang and Gong, Yuqi and Heng, Pheng-Ann and Hsiao, Janet H. and Dou, Qi },
                   title = { { Weakly-supervised Medical Image Segmentation with Gaze Annotations } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Eye gaze that reveals human observational patterns has increasingly been incorporated into solutions for vision tasks. Despite recent explorations on leveraging gaze to aid deep networks, few studies
  exploit gaze as an efficient annotation approach for medical image segmentation which typically entails heavy annotating costs. In this paper, we propose to collect dense weak supervision for medical image segmentation with a gaze annotation scheme. To train with gaze, we propose a multi-level framework that trains multiple networks from discriminative human attention, simulated with a set of pseudo-masks derived by applying hierarchical thresholds on gaze heatmaps. Furthermore, to mitigate gaze noise, a cross-level consistency is exploited to regularize overfitting noisy labels, steering models toward clean patterns learned by peer networks. The proposed method is validated on two public medical datasets of polyp and prostate segmentation tasks. We contribute a high-quality gaze dataset entitled GazeMedSeg as an extension to the popular medical segmentation datasets. To the best of our knowledge, this is the first gaze dataset for medical image segmentation. Our experiments demonstrate that gaze annotation outperforms previous label-efficient annotation schemes in terms of both performance and annotation time. Our collected gaze data and code are available at: https://github.com/med-air/GazeMedSeg.
title: Weakly-supervised Medical Image Segmentation with Gaze Annotations
authors: Zhong, Yuan Tang, Chenhui Yang, Yumeng Qi, Ruoxi Zhou, Kang Gong, Yuqi Heng, Pheng-Ann Hsiao, Janet H. Dou, Qi