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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jiangwei Tu Feng Liu Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Visual relation detection aims to describe the relationships between objects in a scene by using the form of a triplet . Existing methods not only suffer from the huge number of combinations of triples, but also make the context of most of the object pairs in a scene non-discriminative due to simply using the union box of the object pair. In this work, we propose a novel two-stage network. In the first stage, the multi-modal feature fusion strategy is used to complete the correlation detection of object pairs, which reduces the number of triples and enables the model to selectively focus on the relationship between strongly correlated objects in the scene. The object-directed context module (OCM) and global message passing (GMP) introduced in the second stage to adaptively learning the higher-order context of each object pair in the scenario. We evaluate our method on two widely used datasets: Visual Relationship Detection (VRD) and Visual Genome(VG) datasets. Experimental results verify the effectiveness of our method, e.g., the top 100 phrases detection recall improves from 40.01% to 41.21% on the VRD dataset.

关键词： Visualization Adaptation models Correlation Filtering Message passing Genomics Signal processing Feature extraction Context modeling Biomedical engineering

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

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

作者： Zou, Shun Zhang, Zhuo Gao, Guangwei College of Artificial Intelligence Nanjing Agricultural University Nanjing China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9798350368741

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba. © 2025 IEEE.

关键词： Mamba OCTA image State Space Model

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UltraEdit: Instruction-based Fine-Grained Image Editing at Scale 38

UltraEdit: Instruction-based Fine-Grained Image Editing at S...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zhao, Haozhe Ma, Xiaojian Chen, Liang Si, Shuzheng Wu, Rujie An, Kaikai Yu, Peiyu Zhang, Minjia Li, Qing Chang, Baobao National Key Laboratory for Multimedia Information Processing Peking University China State Key Laboratory of General Artificial Intelligence BIGAI China School of Software and Microelectronics Peking University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Peking University China UCLA United States UIUC United States

This paper presents ULTRAEDIT, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix [10] and MagicBrush [71], and provide a systematic approach to producing massive and high-quality image editing samples. ULTRAEDIT offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters;2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models;3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on ULTRAEDIT set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models are available in ***/pkunlp-icler/UltraEdit. © 2024 Neural information processing systems foundation. All rights reserved.

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 214-224页

作者： Zhao, Ying Ge, Luhao Xie, Huixuan Bai, Genghuai Zhang, Zhao Wei, Qiang Lin, Yun Liu, Yuchao Zhou, Fangfang School of Computer Science and Engineering Central South University Changsha China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China College of Information and Communication Engineering Harbin Engineering University Harbin China China Research Institute of Radiowave Propagation Qingdao China

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses. © 2022 IEEE.

关键词： Data visualization

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PRIDE: Pediatric Radiological Image Diagnosis Engine Guided by Medical Domain Knowledge

PRIDE: Pediatric Radiological Image Diagnosis Engine Guided ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Yuze Pang, Shijie Wang, Zhuoyao He, Wen Wang, Yingwen Zhao, Rui-Wei Feng, Rui Zhang, Xiaobo Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China Children's Hospital Fudan University National Children's Medical Center China

ISBN: (纸本)9798350386226

Pediatric chest X-rays (CXRs) are crucial for diagnosing respiratory diseases in children. However, most deep learning models perform poorly on pediatric data due to domain gaps, as they are primarily trained on adult datasets with limited pediatric samples. Recent large models, though effective, still struggle with domain-specific terminology and specialized medical reasoning essential for pediatric diagnoses. To address these challenges, we propose PRIDE: a two-stage Pediatric Radiological Image Diagnosis Engine guided by medical knowledge. PRIDE works consistently with real clinical workflows by first gathering medical evidence from patient data, followed by applying clinical knowledge to enhance diagnostic accuracy. In the Multi-source Radiological Findings Recognition Stage, PRIDE integrates insights from generalized medical models and fine-tuned adult and pediatric radiological models to provide comprehensive findings results. In the Knowledge and Evidence-guided Diagnosis Stage, a Multimodal Large Language Model (MLLM) acts as a pediatric clinician, making diagnostic decisions using CXRs, radiological findings, demographic data, and clinical knowledge. Our evaluation on the VinDr-PCXR pediatric dataset demonstrates that PRIDE outperforms existing methods. Ablation studies further confirm the importance and effectiveness of its key components. © 2024 IEEE.

关键词： Pediatrics

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MOL-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective 41

MOL-AE: Auto-Encoder Based Molecular Representation Learning...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Junwei Zheng, Kangjie Long, Siyu Nie, Zaiqing Zhang, Ming Dai, Xinyu Ma, Wei-Ying Zhou, Hao School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Tsinghua University China PharMolix Inc. China

3D molecular representation learning has gained tremendous interest and achieved promising performance in various downstream tasks. A series of recent approaches follow a prevalent framework: an encoder-only model coupled with a coordinate denoising objective. However, through a series of analytical experiments, we prove that the encoder-only model with coordinate denoising objective exhibits inconsistency between pre-training and downstream objectives, as well as issues with disrupted atomic identifiers. To address these two issues, we propose MOL-AE for molecular representation learning, an auto-encoder model using positional encoding as atomic identifiers. We also propose a new training objective named 3D Cloze Test to make the model learn better atom spatial relationships from real molecular substructures. Empirical results demonstrate that MOL-AE achieves a large margin performance gain compared to the current state-of-the-art 3D molecular modeling approach. The source codes of MOL-AE are publicly available at https://***/yjwtheonly/MolAE. Copyright 2024 by the author(s)

关键词： Atoms

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Dynamic Mask Curriculum Learning for Non-Autoregressive Neural Machine Translation 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Wang, Yisong Hou, Hongxu Sun, Shuo Wu, Nier Jian, Weichen Yang, Zongheng Wang, Pengcong National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

Non-autoregressive neural machine translation is gradually becoming a research hotspot due to its advantages of fast decoding. However, the increase of decoding speed is often accompanied by the loss of model performance. The main reason is that the target language information obtained at the decoder side is insufficient, and the mandatory parallel decoding leads to a large number of mistranslation and missing translation problems. In order to solve the problem of insufficient target language information, this paper proposes a dynamic mask curriculum learning approach to provide target side language information to the model. The target side self-attention layer is added in the pre-training phase to capture the target side information and adjust the amount of information input at any time by way of curriculum learning. The fine-tuning and inference phases disable the module in the same way as the normal NAT model. In this paper, we experiment on two translation datasets of WMT16, and the BLEU improvement reaches 4.4 without speed reduction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Curricula

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A Compact Broadband Metasurface Antenna Based on Hybrid Via-Wall Structures

A Compact Broadband Metasurface Antenna Based on Hybrid Via-...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Chen, Dongxu Liu, Haotian Yang, Wanchen Chen, Jiaqi Cao, Ning Che, Wenquan Xue, Quan Computer and Information College Hohai University Jiangsu Nanjing210098 China The School of Electronic and Information Engineering South China University of Technology Guangdong Key Laboratory of Millimeter-Waves and Terahertz Guangzhou510641 China

ISBN: (纸本)9781733509657

In this work, a compact broadband metasurface (MTS) antenna using hybrid via-wall structures is firstly proposed. First, different via-wall MTS structures are derived from a conventional square MTS structure by folding the square patches downward and loading stacked patches horizontally in various configurations. These modifications effectively lower the resonance frequency and enable miniaturization to varying degrees. Furtherly, for a multi-resonance wideband feature, a hybrid MTS structure consisting of different via-wall structures is presented and then utilized to construct a MTS-based antenna by employing a slot-coupled feeding structure. With these hybrid via-wall structures that resonant at different frequencies, the MTS-based antenna can exhibit a wideband impedance matching. Finally, Theoretical results demonstrate that the proposed antenna achieves remarkable performance. It features a wide impedance bandwidth of 53.4% (5.419.35GHz) and delivers a high gain of approximately 5.9dBi within a compact overall size only 0.44λ0× 0.44λ0× 0.07 λ0 © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Slot antennas

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A thermo-responsive rewritable plasmonic bio-memory by regulating single core-satellite gold nanocluster dissociation

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Nano Research 2024年第8期17卷 7275-7282页

作者： Wen Zhang Yi Wang Yamin Wang Xiaomei Lu Weibing Wu Quli Fan Lei Zhang State Key Laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(IAM) Nanjing University of Posts&TelecommunicationsNanjing 210023China Jiangsu Co-Innovation Center for Efficient Processing and Utilization of Forest Resources College of Light Industry and Food EngineeringNanjing Forestry UniversityNanjing 210037China Zhengzhou Institute of Biomedical Engineering and Technology Zhengzhou 450001China Key Laboratory of Flexible Electronics(KLOFE) Institute of Advanced Materials(IAM)&School of Flexible Electronics(Future Technologies)Nanjing Tech University(Nanjing Tech)Nanjing 211816China

A thermo-responsive rewritable plasmonic bio-memory chip has been successfully fabricated on an indium tin oxide(ITO)glass slide by assembling core-satellite gold nanoclusters with different size of gold nanoparticles(AuNPs)using double-strand DNA(dsDNA)*** the prepared 70@DNA20@13 gold nanoclusters(AuNCs)exhibited more stable and greater photothermal conversion *** short time irradiation by 633 nm microbeam laser,every individual AuNCs could be excited and remove the satellite AuNPs on its ***,in the dissociation process of AuNCs with 3−5 satellite,its color would change from yellow to green,which showed more significant reduction in the red channel of the dark-field microscopy(DFM)images and could be defined to state“0”and“1”***,this plasmonic nano bio-memory could transform cyclically its state between 0 and 1 which exhibited excellent rewritable ability.

关键词： plasmonic assembly thermal-responsive single nanoparticle rewritable bio-memory

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