4 accepted papers
Semi-supervised learning (SSL) is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation (MoS). However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categor…
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-of-the-art Meta RL algorithms require the meta-training tasks to have a dense coverage of the task distribution and a gre
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these methods heavily rely on voxel-based representatio…
Meta Reinforcement Learning (Meta-RL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniques. Probabilistic embeddings for actor-critic \boldsymbol{RL}\boldsymbol{RL} (PEARL) is a leading approach for multi-MD…