Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation

Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts and utilizes unlabeled data which is much easier to acquire. Among existing perturbed consistency learning methods, mean-teacher model serves as a standard baseline for semi-supervised medical image segmentation. In this paper, we present a simple yet efficient competitive ensembling teacher student framework for semi-supervised for left atrium segmentation from 3D MR images, in which two student models with different task-level disturbances are introduced to learn mutually, while a competitive ensembling strategy is performed to ensemble more reliable information to teacher model. Different from the one-way transfer between teacher and student models, our framework facilitates the collaborative learning procedure of different student models with the guidance of teacher model and motivates different training networks for a competitive learning and ensembling procedure to achieve better performance. We evaluate our proposed method on the public Left Atrium (LA) dataset and it obtains impressive performance gains by exploiting the unlabeled data effectively and outperforms several existing semi-supervised methods.Comment: Accepeted for BIBM 202

Research progress on the correlation between gut microbiota and preeclampsia: microbiome changes, mechanisms and treatments

Preeclampsia is a specific disease during pregnancy and is a significant factor in the increased mortality in perinatal women. Gut microbiota, an intricate and abundant microbial community in the digestive tract, is crucial for host metabolism, immunity, and nutrient absorption. The onset and progression of preeclampsia are closely correlated with the changes in maternal gut microbiota. Research purpose was to compile the existing bits of present scientific data and to close the gap in the knowledge of changes in gut microbiota in preeclampsia and their association with preeclampsia. We searched studies from two electronic databases (PubMed and Web of Science) included from 2014 to 2023. This review is divided into three parts. In the first part, the author elaborates longitudinal differences of maternal gut microbiota during different gestation periods. In the second part, we discuss that gut microbiota can lead to the occurrence of preeclampsia by systemic immune response, influencing the release of active peptides, short-chain fatty acids, trimethylamine-N-oxide (TMAO) and other metabolites, vascular factors and Microorganism-immune axis. In the third part, we proposed that a high-fiber diet combined with drugs and microecological regulators may be therapeutic in enhancing or preventing the emergence and evolution of preeclampsia, which needs further exploration. Although the pathogenesis of preeclampsia is still nebulous and there is no clear and valid clinical treatment, our study provides new ideas for the pathogenesis, prevention and treatment of preeclampsia

A Guideline for Silicon Carbide MOSFET Thermal Characterization based on Source-Drain Voltage

Thermal transient measurement based on source-drain voltage is a standard method to characterize thermal properties of silicon semiconductors but is doubtful to be directly applied to silicon carbide (SiC) devices. To evaluate its feasibility and limitations, this paper conducts a comprehensive investigation into its accuracy, resolution, and stability towards yielding the structure information of SiC MOSFET using the source-drain voltage as the temperature sensitive electrical parameter. The whole characterization process involves two main procedures and associated key testing parameters, such as gate voltages, sensing and heating currents, etc. Their impacts on both the static and dynamic performances are also investigated with the aim of providing a guideline for conducting a reproducible thermal transient measurement for SiC MOSFETs