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Found 7 papers, 1 papers with code
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning
We set up a parameter tuning policy network (PTPN), which maps an CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted.
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
Medical Physics
Intelligent Inverse Treatment Planning via Deep Reinforcement Learning, a Proof-of-Principle Study in High Dose-rate Brachytherapy for Cervical Cancer
While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high plan quality is performed by human.
Medical Physics
Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer
The hybrid method provides a more accurate way for predicting LNM using PET and CT.
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks
The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data.
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images.
