Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation


  • Junwen Pan Xiaohe Healthcare, ByteDance College of Intelligence and Computing, Tianjin University
  • Qi Bi School of Remote Sensing and Information Engineering, Wuhan University
  • Yanzhan Yang Xiaohe Healthcare, ByteDance
  • Pengfei Zhu College of Intelligence and Computing, Tianjin University
  • Cheng Bian Xiaohe Healthcare, ByteDance



Computer Vision (CV)


Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to performance degradation. To address this problem, we propose a novel label-efficient hybrid-supervised framework, which considers each weakly-annotated instance individually and learns its weight guided by the gradient direction of the strongly-annotated instances, so that the high-quality prior in the strongly-annotated instances is better exploited and the weakly-annotated instances are depicted more precisely. Specially, our designed dynamic instance indicator (DII) realizes the above objectives, and is adapted to our dynamic co-regularization (DCR) framework further to alleviate the erroneous accumulation from distortions of weak annotations. Extensive experiments on two hybrid-supervised medical segmentation datasets demonstrate that with only 10% strong labels, the proposed framework can leverage the weak labels efficiently and achieve competitive performance against the 100% strong-label supervised scenario.




How to Cite

Pan, J., Bi, Q., Yang, Y., Zhu, P., & Bian, C. (2022). Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 2026-2034.



AAAI Technical Track on Computer Vision II