This paper introduces 2D Gaussian Splatting (GS) to Electrical Impedance Tomography (EIT), marking its first application in this field. Initially developed for computer vision tasks such as scene reconstruction, GS enables continuous representation and efficient rendering of high-resolution images. Building on these capabilities, we propose a novel GS-based EIT reconstruction framework that models conductivity distributions as a set of Gaussian kernels. These kernels act as localized basis functions, dynamically adjusting their parameters (e.g., position, covariance, and amplitude) to enhance representation accuracy. To ensure regularization and physical constraints, we integrate a threshold-adjusted ReLU activation function to filter out insignificant components and a Sigmoid function to constrain conductivity values within a valid physical range. Experimental results on both simulated and real datasets demonstrate that our approach outperforms traditional model-driven methods and is competitive with conventional neural network-based methods in reconstruction quality. Furthermore, systematic ablation studies confirm the effectiveness of the key components of our framework. This work opens new possibilities for integrating advanced rendering techniques into EIT and inverse problem solving, bridging the gap between computer vision and biomedical imaging.

A large amount of medical image data about patients can be shared and applied to build a medical image AI system, which is conducive to rapid and accurate diagnosis and treatment of diseases. However, privacy leakage and data heterogeneity problems in the data-sharing process pose challenges to model construction. To address the above problems, a model construction method with privacy protection under cloud edge architecture is proposed. First, the edge server model is partitioned into two parts during the training process, the output representation of the first part of the model is fitted by generative adversarial network, and then the model parameters with the latter part are uploaded to the central cloud server after differential privacy processing to aggregate and generate the global model and send it down. The experimental analysis of the proposed scheme can realize medical data sharing while protecting the privacy of medical data.

MOTIVATION: The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. RESULTS: We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. AVAILABILITY AND IMPLEMENTATION: The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.