The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multi-center studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites.
View Article and Find Full Text PDFMicrowave-induced thermoacoustic (TA) imaging (MTAI) combines pulsed microwave excitation and ultrasound detection to provide high contrast and spatial resolution images through dielectric contrast, which holds great promise for clinical applications. However, artifacts caused by microwave dielectric effect will seriously affect the accuracy of MTAI images that will hinder the clinical translation of MTAI. In this work, we propose a deep learning-based method fully dense generative adversarial network (FD-GAN) for removing artifacts caused by microwave dielectric effect in MTAI.
View Article and Find Full Text PDFBackground: Breast cancer is the most commonly diagnosed neoplasm in women worldwide. New molecular biomarkers and effective prognostic models are being developed. This study aimed to investigate the clinical and prognostic significance of NUAK2 expression in patients with breast cancer.
View Article and Find Full Text PDFBackgound: Silent electrocardiographic ST change predicts future coronary heart disease (CHD) incidence and mortality, but the prognostic significance of painless ST-segment depression (STD) with respect to sudden cardiac death (SCD) in subjects without apparent CHD remain unclear. This study sought to test the association between non-ischemic resting STD and risk of SCD in the general population.
Methods: A total of 14,935 middle-aged subjects from the prospective, population-based Atherosclerosis Risk in Communities (ARIC) study were included in this analysis.