bibtext: @InProceedings{ Wan_Progressively_MICCAI2024,
                   author = { Wang, Yaqi and Cao, Peng and Hou, Qingshan and Lan, Linqi and Yang, Jinzhu and Liu, Xiaoli and Zaiane, Osmar R. },
                   title = { { Progressively Correcting Soft Labels via Teacher Team for Knowledge Distillation in Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: State-of-the-art knowledge distillation (KD) methods aim to capture the underlying information within the teacher and explore effective strategies for knowledge transfer.
  However, due to challenges such as blurriness, noise, and low contrast inherent in medical images, the teacher’s predictions (soft labels) may also include false information, thus potentially misguiding the student’s learning process.
  Addressing this, we pioneer a novel correction-based KD approach (PLC-KD) and introduce two assistants for perceiving and correcting the false soft labels.
  More specifically, the false-pixel-aware assistant targets global error correction, while the boundary-aware assistant focuses on lesion boundary errors.
  Additionally, a similarity-based correction scheme is designed to forcefully rectify the remaining hard false pixels.
  Through this collaborative effort, teacher team (comprising a teacher and two assistants) progressively generates more accurate soft labels, ensuring the “all-correct” final soft labels for student guidance during KD. 
  Extensive experimental results demonstrate that the proposed PLC-KD framework attains superior performance to state-of-the-art methods on three challenging medical segmentation tasks.
title: Progressively Correcting Soft Labels via Teacher Team for Knowledge Distillation in Medical Image Segmentation
authors: Wang, Yaqi Cao, Peng Hou, Qingshan Lan, Linqi Yang, Jinzhu Liu, Xiaoli Zaiane, Osmar R.