SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images

arXiv:2403.16009v1 Announce Type: new
Abstract: Recently, machine learning-based semantic segmentation algorithms have demonstrated their potential to accurately segment regions and contours in medical images, allowing the precise location of anatomical structures and abnormalities. Although medical images are difficult to acquire and annotate, semi-supervised learning methods are efficient in dealing with the scarcity of labeled data. However, overfitting is almost inevitable due to the limited images for training. Furthermore, the intricate shapes of organs and lesions in medical images introduce additional …

abstract algorithms arxiv boost cs.cv image images labels location machine machine learning medical meta mixed segment segmentation semantic semi-supervised semi-supervised learning supervised learning type