42 citations · 72 across the 5 of their papers we have counts for
5 papers
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis +22
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which i…
Improving Reproducibility and Performance of Radiomics in Low Dose CT using Cycle GANs
Junhua Chen, Leonard Wee, Andre Dekker +1
As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low do…
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study
Junhua Chen, Inigo Bermejo, Andre Dekker +1
Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical…
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study
Junhua Chen, Chong Zhang, Alberto Traverso +4
Radiomics is an active area of research in medical image analysis, the low reproducibility of radiomics has limited its applicability to clinical practice. This issue is especially…
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
Junhua Chen, Haiyan Zeng, Chong Zhang +4
Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer computer aided diagnosis (CAD) can play a crucial role. However, most published CAD methods tr…