Unsupervised shape-and-texture-based generative adversarial tuning of pre-trained networks for carotid segmentation from 3D ultrasound images.

Background: Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensitive to the change of carotid atherosclerosis in response to medical/dietary interventions. Manual segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) required to obtain these metrics is time-consuming and prone to observer variability. Although supervised deep-learning segmentation models have been proposed, training of these models requires a sizeable manually segmented training set, making larger clinical studies prohibitive.

Global Analysis of 2-Hydroxy Fatty Acids by Gas Chromatography-Tandem Mass Spectrometry Reveals Species-Specific Enrichment in Echinoderms.

Abnormal levels of 2-hydroxy fatty acids (2-OH FAs) are characterized in multiple diseases, and their quantification in foodstuffs is critical to identify the sources of supplementation for potential treatment. However, due to the structural complexity and limited available standards, the comprehensive profiling of 2-OH FAs remains an ongoing challenge. Herein, an innovative approach based on gas chromatography-tandem mass spectrometry (GC-MS/MS) was developed to determine the full profile of these FA metabolites.

Meta-Transfer Learning Through Hard Tasks.

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness.