85 citations · 158 across the 5 of their papers we have counts for
5 papers
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…
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…