Achieving accurate predictions with limited samples is a key challenge in biomedical image artificial intelligence. Previous methods rely on pre-trained image foundation models with supervised fine-tuning (SFT) or zero-shot inference to enhance small-data performance. However, SFT is time-consuming and prone to overfitting, whereas zero-shot inference fails to fully exploit available data. Inspired by recent tabular foundation models, which show superior performance on small-sample tasks with in-context learning (ICL), we propose TabiMed, a novel framework that transforms visual representations into structured tabular data, leveraging pre-trained tabular models for fast and accurate analysis on small data. TabiMed consists of three key components: dynamic modality-aware representation engine, tabularization adapter and in-context inference module. Experiments on 10 datasets from different fields demonstrate three major advantages of TabiMed: 1) excellent performance on small datasets, with an average AUC of 14.1% higher than zero-shot; 2) high efficiency, with a training time 250x faster than SFT; 3) scalability to larger datasets through our tabularization adapter. TabiMed proposes a novel pathway to address the challenges of analyzing biomedical images with few samples.

Autonomous underwater vehicles (AUVs) play a pivotal role in the underwater Internet of Things (IoT). However, their capacity to fulfil large-scale bathymetric mapping is often constrained by limitations in navigational capabilities. This article proposes a homogeneous distributed collaborative mapping system, consisting of bathymetric mapping vehicles and its isomorphic server vehicle. The system achieves accurate positioning through bathymetric cooperative active simultaneous localization and mapping (BCA-SLAM) technology. This article mainly focuses on the server’s online path planning in BCA-SLAM using D-optimality metrics of the Fisher information matrix (FIM), to maximize the positioning accuracy of the collaborative system. A method for predicting the intervehicle loop-closure factor FIM was proposed for selecting the subsequent target point for the server, while a lemma for multiple augmented matrix determinants was devised to mitigate its computational burden. Experimental results have proved both the accuracy and efficiency of the proposed algorithm have been tested in semi-physical simulation.

Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization. In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions, and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision.

Abstract According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

Diagnosis of cancerous diseases relies on digital histopathology images from stained slides. However, the staining varies among medical centers, which leads to a domain gap of staining. Existing generative adversarial network (GAN) based stain transfer methods highly rely on distinct domains of source and target, and cannot handle unseen domains. To overcome these obstacles, we propose a self-supervised disentanglement network (SDN) for domain-independent optimization and arbitrary domain stain transfer. SDN decomposes an image into features of content and stain. By exchanging the stain features, the staining style of an image is transferred to the target domain. For optimization, we propose a novel self-supervised learning policy based on the consistency of stain and content among augmentations from one instance. Therefore, the process of training SDN is independent on the domain of training data, and thus SDN is able to tackle unseen domains. Exhaustive experiments demonstrate that SDN achieves the top performance in intra-dataset and cross-dataset stain transfer compared with the state-of-the-art stain transfer models, while the number of parameters in SDN is three orders of magnitude smaller parameters than that of compared models. Through stain transfer, SDN improves AUC of downstream classification model on unseen data without fine-tuning. Therefore, the proposed disentanglement framework and self-supervised learning policy have significant advantages in eliminating the stain gap among multi-center histopathology images.

Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations.

Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high-frequency oscillations (HFOs) in epileptic electroencephalogram signals can be used as an essential biomarker for locating EF. This paper proposes a new method for rapid localization of EF based on the automatic detection of HFOs by waveform feature templates (WFTs). First, the initial screening of HFOs based on Hilbert transform and subsequent rescreening with short-time energy and short-time Fourier transform is performed, and the two screening results are used as the template data set of HFOs. Then, a coarse-grained and fine-grained screening method for detecting HFOs using autocorrelation coefficients and interrelation coefficients as WFT detectors, respectively. Compared with the Hilbert transform detector and other HFOs detector methods proposed at abroad in recent years, the experimental simulations showed that the automatic detector based on WFT could detect HFOs more rapidly, accurately, and efficiently. Our proposed WFT detector has the advantages of high specificity, high sensitivity, and high accuracy in locating EF and has a high clinical utility.

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value.

In shifted Luby transform (SLT) codes, robust soltion distribution (RSD) degree distribution was conducted with shifted rounding to derive shifted RSD (SRSD) degree distribution based on partial information. In shifted rounding process, the large shift of corresponding probability distribution destroyed belief propagation decoding rule, so the decoding symbols increased. Meanwhile, the overlarge probability distribution value of degree k resulted in the increase of decoding symbols. In this work, traditional SRSD degree distribution was developed to improved SRSD (I‐SRSD) degree distribution function by decreasing degree shift of rounding and limiting probability distribution of degree k . Theoretical analysis and experimental results show that SLT codes by I‐SRSD degree distribution can decrease decoding symbols as well as encoding and decoding complexity.