In this work, we propose a novel asymmetric contrastive learning framework named JCL for medical image segmentation with self-supervised pre-training.
To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.
Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch.
We demonstrate a fully-integrated multipurpose microwave frequency identification system on silicon-on-insulator platform.
Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99. 45\% of the averaged optimal solutions).