• We propose a novel enhancing edge-aware neural network for multi-modal 3D medical image segmentation. • A feature progressive co-aggregation strategy for improving feature representation and edge voxel classification. • Compared with the most advanced methods, our model achieves better performance and generalization ability. • We construct a challenging clinical diagnostic dataset of PET images for mantle cell lymphoma. 3D segmentation is critically essential in the clinical medical field, which aids physicians in locating lesions and assists in clinical decision-making. The unique properties of organ and tumour images with large-scale variations and low-edge pixel-level contrast make clear segment edges difficult. Facing these problems, we propose an Enhancing Edge-aware Medical Image Seg mentation (E2MISeg) for smooth segmentation in boundary ambiguity. Firstly, we propose the Multi-level Feature Group Aggregation (MFGA) module to enhance the accuracy of edge voxel classification through the boundary clue of lesion tissue and background. Secondly, to minimize the influence of background noise on the model’s sensitivity to the foreground, the Hybrid Feature Representation (HFR) block utilizes an interactive CNN and Transformer to deeply mine the lesion area and edge texture features while providing more clues for the MFGA module. Finally, we introduce the Scale-Sensitive (SS) loss function that dynamically adjusts the weights assigned to targets based on segmentation errors, with these weights guiding the network to focus on regions where segmentation edges are unclear. Furthermore, we retrospectively collated the Mantle Cell Lymphoma PET Imaging Diagnosis (MCLID) dataset of 176 patients from multiple central hospitals, which enhances our algorithm’s robustness against complex clinical data. The extensive experimental results on three public challenge datasets and the MCLID clinical dataset demonstrate our approach, which outperforms the state-of-the-art methods. Further analysis shows that our components work together to achieve smooth edge segmentation, which is of great significance for accurate clinical diagnosis and prognosis analysis. The Code available at: https://github.com/SoloTillDawn/E2MISeg

General-Purpose Graphics Processing Units (GPGPUs) are crucial for parallel computing in artificial intelligence and big data with their performance heavily relying on efficient warp scheduling. Traditional schedulers, such as Round-Robin (RR) and Greedy-Then-Oldest (GTO), employ static strategies that struggle with adapting to diverse workloads, causing performance disparities across different applications. Prior work has focused on aspects like critical warps and memory access locality but has often overlooked the arithmetic intensity of workloads. Drawing inspiration from the Roofline model and recognizing that different workloads exhibit distinct computational intensities, we propose an Arithmetic Intensity based CacheConscious Adaptive Warp Scheduler (AICAWS). It operates by first analyzing the kernel's static arithmetic intensity through compiler, which serves as a baseline for the hardware. Subsequently, during warp execution, AICAWS dynamically monitors the warp's execution progress, analyzes its runtime arithmetic intensity, and adjusts warp scheduling strategies based on this. Furthermore, AICAWS considers cache locality during warp execution, enabling fine-grained classification of warps based on this. This synergistic mechanism enables AICAWS to effectively hide long-latency memory access operations. Evaluations on diverse benchmarks demonstrate that AICAWS achieves an average performance improvement of 26.3% compared to the baseline scheduler, with a peak improvement of 77.9%.

Kangyu Zhu, Ziyuan Qin, Huahui Yi, Zekun Jiang, Qicheng Lao, Shaoting Zhang, Kang Li. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Efficient detection of near-seafloor metallic targets poses significant challenges for conventional underwater inspection methods. In this paper, the optimal detection frequency of the marine electromagnetic detection technology for detecting small targets such as unexploded ordnance (UXO), iron pipes, and aluminum pipes on the seabed is simulated and analyzed using the Monte Carlo method. This analysis is based on the electromagnetic response law of the target and the attenuation law of the electromagnetic field in the seawater medium in marine electromagnetic detection technology. The optimal detection frequency band is obtained to be [1807, 5365] Hz. Selecting a single frequency point in this frequency band to construct a series resonant circuit can provide the optimal radiant efficiency of the transmitter. On this basis, a remotely operated vehicle (ROV)-based magnetic detection system was designed, using a coil as the transmitter and a fluxgate as the receiver. The system was verified through sea trials, and the detection distance of small aluminum pipe targets can reach over 1.5 m.

In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on constructing robust classifiers to estimate the labeling confidence of candidate labels in order to identify the correct one. However, these methods usually struggle to rectify mislabeled samples. To help existing PLL methods identify and rectify mislabeled samples, in this paper, we introduce a novel partner classifier and propose a novel ``mutual supervision'' paradigm. Specifically, we instantiate the partner classifier predicated on the implicit fact that non-candidate labels of a sample should not be assigned to it, which is inherently accurate and has not been fully investigated in PLL. Furthermore, a novel collaborative term is formulated to link the base classifier and the partner one. During each stage of mutual supervision, both classifiers will blur each other's predictions through a blurring mechanism to prevent overconfidence in a specific label. Extensive experiments demonstrate that the performance and disambiguation ability of several well-established stand-alone and deep-learning based PLL approaches can be significantly improved by coupling with this learning paradigm.

Unsupervised domain adaptive semantic segmentation leverages synthetic data to train a segmentation model and transfers it to unlabeled real images. Due to the style difference, the transferred model suffers from the domain gap. Even worse, some classes exhibit the extreme domain gap, where the feature distributions undergo a complete shift between the two domains. To alleviate it, we propose a domain-debiased self-training strategy with CLIP to distill its domain-agnostic knowledge. Specifically, we enforce the consistency between the feature maps from our segmentation model and the image encoder of CLIP. Meanwhile, the text embeddings from the text encoder for each class serve as a domain-agnostic classifier to support a domain-debiased feature learning condition. Experimental results under standard UDA settings demonstrate that our proposed strategy consistently improves the UDA segmentation performance based on different backbones and with different large pre-trained models.

Anomaly segmentation is a critical task for safety-critical applications, such as autonomous driving in urban environments. Its objective is to detect out-of-distribution (OOD) samples with unseen categories, given a pre-trained segmentation model. The core challenge of this task is how to distinguish hard in-distribution samples from OOD samples, which has not been explicitly discussed in previous research. In this paper, we propose a simple yet effective approach named CosMe (Consensus Synergizes with Memory) to address this challenge. CosMe consists of two key components: 1) building a memory bank comprising seen prototypes extracted from multiple layers of the given segmentation model, and 2) training an auxiliary model that mimics the behavior of the given model and using the consensus of their mid-level features as complementary cues that synergize with the memory bank. The former serves as a baseline that can detect all potential outliers, including both OOD and hard in-distribution samples; the latter assists in distinguishing between these two types of outliers. Experimental results on several urban scene anomaly segmentation datasets demonstrate that CosMe outperforms previous approaches by a significant margin.

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes. To confront the stated problems, we propose a novel Hierarchical-instance Contrastive Learning (HCLe) method for minority detection by only involving data from the majority class in the training stage. To tackle inconsistent intra-class distribution in majority classes, our method introduces two branches, where the first branch employs an auto-encoder network augmented with three constraint functions to effectively extract image-level features, and the second branch designs a novel contrastive learning network by taking into account the consistency of features among hierarchical samples from majority classes. The proposed method is further refined with a diverse mini-batch strategy, enabling the identification of minority classes under multiple conditions. Extensive experiments have been conducted to evaluate the proposed method on three datasets of different diseases and modalities. The experimental results show that the proposed method outperforms the state-of-the-art methods.

The problem of high similarity in attack data leading to unsatisfactory detection results of air traffic control network security attack behavior is addressed. This article designs a new clustering detection method for air traffic control network security attack behavior. Set the characteristic state of air traffic control network security attack behavior, obtain the set of air traffic control network security attack behavior characteristics through recursive feature addition method, and extract the characteristics of air traffic control network security attack behavior by determining the degree of feature criticality. Calculate the expected information gain and entropy value of feature data, determine the information gain of feature data, and reduce the interference of similar feature data. Introduce an automatic encoder in artificial intelligence algorithms to encode and decode the characteristics of air traffic control network security attack behavior, and achieve dimensionality reduction processing of air traffic control network security attack behavior data. Based on the above processing, a Unsupervised learning algorithm for clustering detection of air traffic control network security attacks is designed. Firstly, determine the distance between clustering clusters of air traffic control network security attack behavior characteristics, calculate the clustering threshold, and construct the initial clustering center. Then, recalculate the new mean of all feature objects in each cluster as the new cluster center point. Secondly, traverse all objects in the clustering cluster of air traffic control network security attack behavior feature data. Finally, clustering detection of air traffic control network security attack behavior is completed through the calculation of the objective function. The experiment takes three sets of experimental attack behavior datasets as the test subjects, with detection rate, false detection rate, and recall rate as the test indicators, and selects three similar methods for comparative testing. The experimental results show that the detection rate of the proposed method remains around 98%, the false detection rate remains below 1%, and the recall rate is above 97%. It has been proven that the proposed method can improve the detection performance of air traffic control network security attack behavior.

Radiomics is an important research direction in the field of medical image analysis. Although the number of publications is increasing year by year, it has been difficult to translate into clinical practice due to the small size of clinical data. In most cases, Radiomics data can be considered as small tabular data. Deep learning is often less effective than classical machine learning algorithms in processing tabular data. Recently, table representation learning has started to receive more widespread attention which is often an easily overlooked but very important area of research, helping improve the status of tabular deep learning. Here, we first apply a pre-trained Transformer model named Tabular Prior-Data Fitted Network (TabPFN) to the field of Radiomics analysis. We implement extensive experiments on three real-world clinical datasets: (a) Ultrasound Radiomics dataset for the classificatory diagnosis of Kidney tumor, (b) CT Radiomics dataset for the prediction of EGFR gene mutations in non-small cell lung cancer, (c) MRI Radiomics dataset for the prediction of treatment response of brain metastases to gamma knife radiosurgery. By comprehensive analysis, we demonstrate that the pre-trained tabular Transformer can be used as a realtime, efficient, and stable Radiomics data processor with superior performance over other tabular machine learning methods in different clinical tasks. We also simulate an ideal clinical practice scenario for evaluating the clinical translation potential of pretrained models. Finally, we explore the advantages and limitations of pre-trained tabular models for Radiomics analysis.

When handling complicated text images (e.g., irregular structures, low resolution, heavy occlusion, and uneven illumination), existing supervised text recognition methods are data-hungry. Although these methods employ large-scale synthetic text images to reduce the dependence on annotated real images, the domain gap still limits the recognition performance. Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution. However, existing self-supervised text recognition methods conduct sequence-to-sequence representation learning by roughly splitting the visual features along the horizontal axis, which limits the flexibility of the augmentations, as large geometric-based augmentations may lead to sequence-to-sequence feature inconsistency. Motivated by this, we propose a novel self-supervised Character-to-Character Distillation method, CCD, which enables versatile augmentations to facilitate general text representation learning. Specifically, we delineate the character structures of unlabeled real images by designing a self-supervised character segmentation module. Following this, CCD easily enriches the diversity of local characters while keeping their pairwise alignment under flexible augmentations, using the transformation matrix between two augmented views from images. Experiments demonstrate that CCD achieves state-of-the-art results, with average performance gains of 1.38% in text recognition, 1.7% in text segmentation, 0.24 dB (PSNR) and 0.0321 (SSIM) in text super-resolution. Code is available at https://github.com/TongkunGuan/CCD.

A novel vehicle localization and tracking methods are presented based on magnetic anomaly detection by distributed magnetic sensors. First, taking advantage of total magnetic field, in this article, we propose a total field matching (TFM) method that is immune of rotational vibrations to perform target localization. Instead of directly inverting the nonlinear magnetic dipole equations, we use the TFM approach to find the suboptimal target position, and then apply the linear Kalman filter to tail after the target. Because the relationship is linear between the target dynamics and the localization equations. A case study is performed by simulation to result in an estimated trajectory of (d,ϕ) = (70.8 m, 44.9°) that agrees well with the real one of (d,ϕ) = (70.5 m, 45°). For a vehicle tracking, the outdoors experiment results show good estimation accuracy based on four different sensor networking configurations.

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.

This letter presents a frequency characteristics analysis for a magnetic anomaly detection (MAD) study. A magnetic dipole model is developed to study the energy distribution of the obtained MAD signal in the frequency domain, where$f_{h}$is proposed to define the signal high-frequency boundary. The relations between$f_{h}$and other parameters, such as the velocity of the target (or platform), closest path approach (CPA), magnetic moment strength, magnetic moment orientation (MMO), and sensor orientation, are analyzed. We find that the frequency response is independent of the moment strength. The combined effects of the MMO and sensor orientation on$f_{h}$are defined by the functio$n g(\alpha $,$\beta $,$\theta $,$\phi$), which is studied by the Monte Carlo method, yielding a maximum value of max($g) \approx ~0.85$. Thus, the function$f_{h} = 0.85v$/CPA is advanced for frequency boundary estimation. The proposed theoretical analysis is examined in an experiment to aid in extracting the magnetic anomaly signal effectively.