Multiple-instance ensemble for construction of deep heterogeneous committees for high-dimensional low-sample-size data.

Deep ensemble learning, where we combine knowledge learned from multiple individual neural networks, has been widely adopted to improve the performance of neural networks in deep learning. This field can be encompassed by committee learning, which includes the construction of neural network cascades. This study focuses on the high-dimensional low-sample-size (HDLS) domain and introduces multiple instance ensemble (MIE) as a novel stacking method for ensembles and cascades.

Diagnostic accuracy of apparent diffusion coefficient values combined with γ-glutamyl transpeptidase-to-platelet ratio parameters for predicting hepatitis B-related fibrosis.

Objectives: The accuracy of non-invasive liver fibrosis diagnosis based on the apparent diffusion coefficient (ADC) value combined with the γ-glutamyl transpeptidase-to-platelet ratio (GPR) model to predict the stage of hepatitis B-related fibrosis has not been reported. This study aimed to evaluate the diagnostic efficacy of ADC value combined with GPR for liver fibrosis grading.

Methods: The data of 180 patients with chronic hepatitis B (CHB) diagnosed by liver biopsy were analyzed.