267 citations · 613 across the 11 of their papers we have counts for
10 papers
AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
Xi Chen, Jiahuan Lv, Dehua Feng +4
Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment. Artificial intelligence (AI), especially the deep learning, has a…
Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
Xiao Liang, Liyuan Chen, Dan Nguyen +5
Cone beam computed tomography (CBCT) images can be used for dose calculation in adaptive radiation therapy (ART). The main challenges are the large artefacts and inaccurate Hounsfi…
Combining Many-objective Radiomics and 3-dimensional Convolutional Neural Network through Evidential Reasoning to Predict Lymph Node Metastasis in Head and Neck Cancer
Liyuan Chen, Zhiguo Zhou, David Sher +5
Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimizati…
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
Shulong Li, Panpan Xu, Bin Li +8
To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the ou…
Multifactorial cancer treatment outcome prediction through multifaceted radiomics
Zhiguo Zhou, David Sher, Qiongwen Zhang +6
Accurately predicting the treatment outcome plays a greatly important role in tailoring and adapting a treatment planning in cancer therapy. Although the development of different m…
Automatic multi-objective based feature selection for classification
Zhiguo Zhou, Shulong Li, Genggeng Qin +3
Objective: Accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate…
Reliable Gene Mutation Prediction in Clear Cell Renal Cell Carcinoma through Multi-classifier Multi-objective Radiogenomics Model
Xi Chen, Zhiguo Zhou, Raquibul Hannan +6
Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Because the complete gene mutational landscape cannot be characteri…
Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning
Zhiguo Zhou, Liyuan Chen, David Sher +5
Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential for treatment optimizati…
Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion
Zhiguo Zhou, Zhi-Jie Zhou, Hongxia Hao +5
Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer t…
Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer
Hongxia Hao, Zhiguo Zhou, Shulong Li +10
Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and…