BoostMIS：利用自适应伪标记和信息主动标注增强医学图像半监督学习
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation
摘要：
本文提出了一种新的半监督学习（SSL）框架BoostMIS，该框架结合了自适应伪标记和信息主动标注，充分发挥了医学图像SSL模型的潜力：（1）BoostMIS能够根据当前学习状态自适应地利用聚类假设和未标记数据的一致性正则化。该策略可以自适应地生成从任务模型预测转换的独热“硬”标签，以更好地进行任务模型训练。(2)对于低置信度的未标记图像，我们引入了主动学习（AL）算法，通过利用虚拟对抗扰动和模型的密度感知熵来寻找信息样本作为注释候选。这些信息候选者随后被馈送到下一个训练周期，以更好地进行SSL标签传播。值得注意的是，自适应伪标记和信息主动注释形成了相互协作以提高医学图像SSL的学习闭环。为了验证所提出的方法的有效性，我们收集了转移性硬膜外脊髓压迫（MESCC）数据集，旨在优化MESCC诊断和分类，以改善专科转诊和治疗。我们在MESCC和另一个公共数据集COVIDx上对BoostMIS进行了广泛的实验研究。实验结果验证了我们的框架的有效性和普适性不同的医学图像数据集与各种国家的最先进的方法有显着的改善。
英文摘要：
In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status. This strategy can adaptively generate one-hot "hard" labels converted from task model predictions for better task model training. (2) For the unselected unlabeled images with low confidence, we introduce an Active learning (AL) algorithm to find the informative samples as the annotation candidates by exploiting virtual adversarial perturbation and model's density-aware entropy. These informative candidates are subsequently fed into the next training cycle for better SSL label propagation. Notably, the adaptive pseudo-labeling and informative active annotation form a learning closed-loop that are mutually collaborative to boost medical image SSL. To verify the effectiveness of the proposed method, we collected a metastatic epidural spinal cord compression (MESCC) dataset that aims to optimize MESCC diagnosis and classification for improved specialist referral and treatment. We conducted an extensive experimental study of BoostMIS on MESCC and another public dataset COVIDx. The experimental results verify our framework's effectiveness and generalisability for different medical image datasets with a significant improvement over various state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
