Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation
Keywords:Computer Vision (CV)
AbstractDue 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. https://doi.org/10.1609/aaai.v36i2.20098
AAAI Technical Track on Computer Vision II