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SAM-MedUS: a foundational model for universal ultrasound image segmentation

Journal of Medical Imaging 2025年第2期12卷 027001页

作者： Tian, Feng Zhai, Jintao Gong, Jinru Lei, Weirui Chang, Shuai Ju, Fangfang Qian, Shengyou Zou, Xiao Hunan Normal University The School of Physics and Electronics Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

Purpose: Segmentation of ultrasound images for medical diagnosis, monitoring, and research is crucial, and although existing methods perform well, they are limited by specific organs, tumors, and image devices. Applications of the Segment Anything Model (SAM), such as SAM-med2d, use a large number of medical datasets that contain only a small fraction of the ultrasound medical images. Approach: In this work, we proposed a SAM-MedUS model for generic ultrasound image segmentation that utilizes the latest publicly available ultrasound image dataset to create a diverse dataset containing eight site categories for training and testing. We integrated ConvNext V2 and CM blocks in the encoder for better global context extraction. In addition, a boundary loss function is used to improve the segmentation of fuzzy boundaries and low-contrast ultrasound images. Results: Experimental results show that SAM-MedUS outperforms recent methods on multiple ultrasound datasets. For the more easily datasets such as the adult kidney, it achieves 87.93% IoU and 93.58% dice, whereas for more complex ones such as the infant vein, IoU and dice reach 62.31% and 78.93%, respectively. Conclusions: We collected and collated an ultrasound dataset of multiple different site types to achieve uniform segmentation of ultrasound images. In addition, the use of additional auxiliary branches ConvNext V2 and CM block enhances the ability of the model to extract global information and the use of boundary loss allows the model to exhibit robust performance and excellent generalization ability. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Image segmentation

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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

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Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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Prototype Matching with Domain Alignment for Open-world Specific Emitter Identification

Prototype Matching with Domain Alignment for Open-world Spec...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xiao, Wang Ye, Yalan Pan, Tongjie Li, Chenyang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Open-world specific emitter identification (SEI) is a practical but challenging task, because it requires accurate identification of both known and unknown emitters in open environments with channel variations. However, traditional closed-set SEI methods suffer from severe performance degradation in open-world environments since 1) closed-set SEI models would misclassify unknown specific emitters (SEs) as known ones, and 2) channel variations would lead to distribution shifts in the radio frequency (RF) fingerprint of known SEs. It is challenging to address both issues, as they coexist and interact with each other. In this paper, we propose a novel prototype matching with domain alignment framework for open-world SEI, which designed to simultaneously address the challenges of unknown SE identification and channel variation. The proposed framework first pretrains a feature extractor and then trains the pretrained feature extractor and an extended open-set classifier via the self-training paradigm. Towards the former, a partial domain alignment strategy is introduced to mitigate the impact of channel variations. Towards the latter, a prototype matching-based pseudo-labeling strategy is proposed to generate reliable pseudo-labels, facilitating subsequent fine-grained identification of unknown SEs. An open-set classification loss is introduced later to help the classifier avoid classifying unknown SEs as known ones. Experiments conducted on a public WiFi dataset demonstrate the superiority and robustness of the proposed method, illustrating its potential application prospects in open-world environments. © 2025 IEEE.

关键词： open-set recognition prototype learning self-training specific emitter identification unsupervised domain adaptation

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3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation 27th

3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsul...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gan, Dongwei Chang, Ming Chen, Juan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031781858

Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requirements for segmentation efficiency. Convolutional neural networks (CNNs) have made considerable achievements in MIS. However, they are difficult to fully collect global context information and their pooling layer may cause information loss. Capsule networks, which combine the benefits of CNNs while taking into account additional information such as relative location that CNNs do not, have lately demonstrated some advantages in MIS. Vision Transformer (ViT) employs transformers in visual tasks. Transformer based on attention mechanism has excellent global inductive modeling capabilities and is expected to capture long-range information. Moreover, there have been resent studies on making ViT more lightweight to minimize model complexity and increase efficiency. In this paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps, which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch finer details for segmentation. We conduct experiments on various datasets, including iSeg-2017, Hippocampus and Cardiac to verify the performance and efficiency of 3D-EffiViTCaps, which performs better than previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We further implement a series of ablation experiments on the main blocks. Our code is available at: https://***/HidNeuron/3D-EffiViTCaps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decoding

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning 24th

Multiple-Round Aggregation of Abstract Semantics for Secur...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Jiao Li, Xiong Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615445

Federated Learning (FL) provides a valuable framework that allows for the collaborative training of models across distributed networks while maintaining the privacy of the data involved. The concept of secure aggregation is crucial in preserving both the privacy and integrity of data within FL systems. However, the deployment of secure aggregation encounters significant barriers. Conventional secure aggregation methods are primarily designed for single-round operations, which can become prohibitively expensive in scenarios that require multiple rounds. Additionally, these methods often struggle in large-scale environments characterized by heterogeneous data and models. This paper introduces the Multiple-Round Aggregation of Abstract Semantics (MRAAS), a novel scheme designed to address the aforementioned limitations in secure aggregation within heterogeneous FL environments. The MRAAS scheme enhances security and reduces the need for extensive data exchanges by aggregating abstracted model semantics instead of raw model gradients, facilitating secure, multiple rounds of aggregation. Key innovations of MRAAS include an advanced abstraction technique, robustness against participant drop-out, and the use of recyclable secrets. These innovations collectively enhance system resilience and reduce the necessity for frequent cryptographic renewals. Our theoretical evaluations focus on the security features of MRAAS. Empirical validations conducted through comparative analyses utilizing two public datasets confirm the superiority of MRAAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Injecting Visual Features into Whisper for Parameter-Efficient Noise-Robust Audio-Visual Speech Recognition

Injecting Visual Features into Whisper for Parameter-Efficie...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Zhao Yeo, Yue Heng Jiang, Rui Fu, Xiao Chen, Weiguang Xi, Wei Zhao, Jizhong School of Computer Science and Technology Xi'an Jiaotong University China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9798350368741

Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or fully finetuning a pre-trained model, both of which incur significant computational costs and are often impractical for large-scale speech foundation models. This gap highlights the need for more efficient methods to leverage visual and acoustic information in AVSR tasks. To address this challenge, we propose AVWhisper, a parameter-efficient model that integrates visual and acoustic representations by injecting visual features from the AV-HuBERT encoder into the pre-trained Whisper model. Our approach leverages the existing attention mechanisms in Whisper to facilitate cross-modal interaction and integrates auxiliary visual information through lightweight adapters based on Low-Rank Adaptation (LoRA) and prompt-based techniques. Furthermore, a two-phase training strategy is adopted to effectively handle cross-domain differences and visual information injection problems respectively. Extensive experiments on the LRS3-TED dataset demonstrate that AVWhisper consistently outperforms state-of-the-art methods across various noise conditions, offering a more efficient and scalable solution for audio-visual speech recognition. © 2025 IEEE.

关键词： Audio-Visual Speech Recognition LoRA Noise-Robust Parameter-Efficient Prompt Whisper

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On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 369-380页

作者： Qian, Yuwen Qiu, Tianyang Ma, Chuan Ni, Yiyang Yuan, Long Zhou, Xiangwei Li, Jun Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Chongqing University College of Computer Science Chongqing400044 China Jiangsu Second Normal University School of Computer Engineering Nanjing210037 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

Intelligent transportation systems grapple with the formidable task of precisely forecasting real-time traffic conditions, where the traffic dynamics exhibit intricacies arising from spatial and temporal dependencies. The urban road network presents a complex web of interconnected roads, where the state of traffic on one road can influence the conditions of others. Moreover, the prediction of traffic conditions necessitates the consideration of diverse temporal factors. Notably, the proximity of a time point to the present moment wields a more substantial impact on subsequent states. In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. In particular, KGCN is employed to capture the correlation of the external knowledge factors for the road and the spatial dependencies, and the gated recurrent unit (GRU) is used to cope with temporal dependence. Furthermore, to improve traffic prediction accuracy, we propose the GRU combined with a selected attention mechanism with Gumble-Max to predict traffic at the temporal dimension, where a selector is chosen to dynamically assign the feature in various time intervals with different weights. Experimental results with real-life data show the proposed KGCN with GSAM can achieve high accuracy in traffic prediction. Compared to the traditional traffic prediction method, the proposed KGCN with GSAM can achieve higher efficacy and robustness when capturing global dynamic temporal dependencies, external knowledge factor correlations, and spatial correlations. © 2023 IEEE.

关键词： Knowledge graph

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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