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Found 9 papers, 2 papers with code
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation .
Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs).
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
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.
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
DiffDP: Radiotherapy Dose Prediction via a Diffusion Model
To alleviate this limitation, we innovatively introduce a diffusion-based dose prediction (DiffDP) model for predicting the radiotherapy dose distribution of cancer patients.
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems.
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
