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Found 7 papers, 0 papers with code
AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment.
AutoMO-Mixer: An automated multi-objective multi-layer perspecton Mixer model for medical image based diagnosis
The experimental study demonstrated that AutoMO-Mixer can obtain better performance compared with Mixer and CNN.
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.
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
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.
Automatic multi-objective based feature selection for classification
Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical.
Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion
First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality are blindly combined into a single predictive model.
