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MAGS: Max-Gap Loss-Guided Siamese-reconstruction Network for Hyperspectral image Partial Label Learning

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Tian, Xiaoyu Luo, Fulin Fu, Chuan Guo, Tan Du, Bo Chanussot, Jocelyn Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Univ Grenoble Alpes Inria CNRS Grenoble INPLJK F-38000 Grenoble France

Due to the powerful feature extraction capabilities of deep learning, a series of deep learning-based methods for hyperspectral image (HSI) classification have been proposed and achieved satisfactory performance. However, most of these methods require a large number of labeled data, and the collection of completely accurate pixel-level labeled HSI data is difficult, resulting from the intricate label ambiguity of HSI and incomplete prior knowledge of annotators. Simultaneously, a few researchers focus on label ambiguity for HSI classification. Partial label learning (PLL) is one of the strategies to solve the problem where each training instance is assigned a candidate label set, among which only one is the ground truth label, which can essentially alleviate labeling difficulties. In this article, a max-gap loss-guided Siamese reconstruction network (MAGS) is proposed to combine PLL with HSI classification. MAGS consists of three components, including a spatial-spectral encoder, a spatial-spectral decoder, and a Siamese spatial-spectral encoder for high-quality feature representation learning to facilitate label disambiguation. In the encoding process, MAGS introduces the cross-attention and max-matching fusion strategies to obtain more representative features. In addition, to improve label disambiguation, the maximum gap loss is designed to guide the model training. Quantitative and qualitative results indicate that the MAGS outperforms several state-of-the-art methods on three HSI datasets. The code is available at https://***/Nemo96yu/MAGS.

关键词： Feature extraction Phase locked loops Training Hyperspectral imaging data mining Representation learning Labeling Electronic mail Recurrent neural networks image reconstruction Hyperspectral image (HSI) classification label disambiguation partial label learning (PLL) spatial-spectral information

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A Spatial Transformation Based Next Frame Predictor

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IEEE ACCESS 2025年 13卷 5406-5423页

作者： Mokssit, Saad Licea, Daniel Bonilla Guermah, Bassma Ghogho, Mounir Int Univ Rabat Coll Engn & Architecture TICLab Rabat 11103 Morocco Mohammed VI Polytech Univ Coll Comp Ben Guerir 43150 Morocco Univ Leeds Fac Engn Leeds LS2 9JT England

In recent years, the automobile industry has achieved astonishing success in making autonomous cars safer, more affordable, and more reliable. However, current autonomous driving technology is mainly based on reactive controllers that attempt to respond to the various events the car encounters. Yet, achieving a truly safe and reliable autonomous system necessitates anticipating such events and planning the correct actions in advance to avoid undesirable behavior. Recent advances in deep learning have shown remarkable performance in predicting future frames from video sequences. However, most of these approaches can only handle a few moving elements in the scene and perform poorly when the camera is in motion. This is mainly due to the difficulty of disentangling camera intrinsic motion from object-dependent motion. In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. Static elements of the scene are then used to estimate camera intrinsic motion, which is applied to the background to predict how it will be viewed in the next frame. Notably, our approach significantly reduces the complexity typically associated with such models by requiring the estimation of only 14 parameters per moving object, independent of image resolution. We have validated the generalization capabilities of our model through training on simulated datasets and testing on real-world datasets. The results indicate that our model not only outperforms existing models trained solely on real data but also exhibits superior resilience to occlusions and incomplete data in the input sequences. These findings underscore the potential of our model to significantly improve the predictive analytics capabilities of autonomous driving systems, thereby enhancing their safety and reliability in dynamic

关键词： Next frame prediction moving camera image reconstruction deep learning projective and affine transforms Next frame prediction moving camera image reconstruction deep learning projective and affine transforms

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MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data

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PHOTOACOUSTICS 2025年 41卷 100679页

作者： Wang, Liangjie Meng, Yi-Chao Qian, Yiming Shanghai Univ Inst Fiber Opt Shanghai 201800 Peoples R China Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Joint Int Res Lab Specialty Fiber Opt & Adv Commun Shanghai 200444 Peoples R China

Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and in vivo datasets demonstrates that our method achieves better reconstructions with improved speed.

关键词： Biomedical imaging Photoacoustic imaging Deep learning Convolutional networks

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Satellite image Inpainting With Edge-Conditional Expectation Attention

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10830-10845页

作者： Zhou, Dazhi Chen, Yanjun Zhang, Yuhong Niu, Jing Harbin Normal University School of Mathematical Sciences Harbin150025 China Harbin Normal University School of Geographical Sciences Harbin150025 China

Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev’s inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Research on depth estimation and human body reconstruction effects in single-image scene reconstruction 2

Research on depth estimation and human body reconstruction e...

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2nd International Conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhou, Xiuyuan Li, Yuanzhen Zhu, Yaling Liu, Ruilin Yang, Lina Jing, Yongxia Lanzhou Institute of Technology Gansu Lanzhou730030 China Yunnan University Yunnan Kunming650091 China Qiongtai Normal University Hainan Haikou571100 China

ISBN: (纸本)9781510689053

With the rapid development of digital technology and deep learning, recovering 3D scene information and reconstructing human bodies from a single image has become a focal point of research in computer vision and computer graphics. This technology has also found widespread application in fields such as cultural relic restoration, autonomous driving, virtual reality, and medical image analysis. In this paper, we explore the challenges posed by the network's contextual perception abilities and the influence of loss functions, which can lead to issues like incomplete depth structures, depth drift, and texture copying. To overcome these obstacles, we propose refined methods that produce highly accurate and structurally sound depth estimates, effectively resolving problems such as texture copying and depth drift. Our methods demonstrate strong generalization capabilities in human depth estimation models, enabling precise depth estimation across various scenarios. © 2025 SPIE.

关键词： Virtual reality

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Photoacoustic image reconstruction with the FD-UGAN model

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Applied Optics 2025年第15期64卷 4152-4159页

作者： Jing ZhangShengxian YanYuyang LeiPenghui WangXiao GaoXiang Li Hui Cao School of Physics and Information Technology Shaanxi Key Laboratory of Ultrasonics Shaanxi Normal University 710100 Shaanxi China

Photoacoustic imaging has been widely used in the biomedical field by virtue of its high resolution and depth imaging advantages, but there are still problems with detector viewing angle limitation and incomplete data due to sparse sampling in the application process. To address this problem, FD-UGAN is proposed as a novel deep learning network, to our knowledge, in which the Full Dense U-Net is initially employed as the generator in a generative adversarial network framework for photoacoustic image reconstruction. The encoding and decoding pathways are redesigned to enable the network to efficiently capture and integrate multi-scale feature information, facilitating high-quality image reconstruction. The experimental results demonstrate that FD-UGAN outperforms existing reconstruction methods on the dataset. The proposed FD-UGAN provides an effective solution for image reconstruction from sparsely sampled data and exhibits significant potential for broader applications.

关键词： Biomedical imaging image reconstruction Magnetic resonance imaging Optical imaging Photoacoustic imaging Viewing angles

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image reconstruction from incomplete projection data using combined ART-CBP algorithm

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DEFENCE SCIENCE JOURNAL 2002年第3期52卷 303-316页

作者： Singh, S Muralidhar, K Munshi, P Indian Inst Technol Kanpur 208016 Uttar Pradesh India

The present work is concerned with reconstructing cross-sections of composite structures when complete projection data is not available from the experiments. A novel approach combining the algebraic reconstruction technique (ART) and convolution back projection (CBP) algorithm has been developed for reconstruction with such data. The numerical results obtained reveal that a combination of ART and CBP is able to reconstruct cross-sections from incomplete data to an, extent that is meaningful for non-destructive evaluation.

关键词： algebraic reconstruction technique convolution back projection combined ART-CBP image reconstruction non-destructive evaluation computerised tomography

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

The proceedings contain 23 papers. The special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical image Computing, Embodied AI and Robotics for HealTHcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. The topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen data Properties with Diffusion Autoencoder Embeddings Using Spine MRI data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction from Histopathology Whole Slide images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第1期29卷 396-408页

作者： Zheng, Yushan Wu, Kun Li, Jun Tang, Kunming Shi, Jun Wu, Haibo Jiang, Zhiguo Wang, Wei Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Image Proc Ctr Sch Astronaut Beijing 100191 Peoples R China Hefei Univ Technol Sch Software Hefei 230009 Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Dept Pathol Div Life Sci & Med Hefei 230036 Peoples R China Univ Sci & Technol China Intelligent Pathol Inst Div Life Sci & Med Hefei 230036 Peoples R China

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0.950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0.853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images.

关键词： Representation learning Task analysis Biomarkers Histopathology Contrastive learning Annotations Feature extraction Gene mutation prediction partial-label learning. representation learning WSI analysis

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Superresolved image reconstruction from incomplete data

Superresolved Image Reconstruction from Incomplete Data

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Conference on image reconstruction from incomplete data vii

作者： Chuang, Yi-Chen Dudley, Richard Fiddy, Michael A. Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Univ N Carolina Dept Phys & Opt Sci Charlotte NC 28223 USA Univ N Carolina Ctr Optoelect & Opt Commun Charlotte NC 28223 USA

ISBN: (纸本)9780819492173

A finite thickness slab of a metamaterial having a refractive index close to n = -1, can be used for sub-wavelength scale imaging. In the image domain, the measured fields contain evanescent wave contributions from subwavelength scale features in the object but these have to be related to the intrinsic parameters describing the scatterer such as refractive index or permittivity. For weak scatterers there can be a simple relationship between the field distribution and the permittivity profile. However for strong (multiple) scatterers and, more importantly, for objects for which subwavelength features contribute to the scattered (near) field, there is no simple relationship between the measured data and the permittivity profile. This is a significant inverse scattering problem for which no immediate solution exists and given the metamaterial slab's limitations one cannot assume that either angle or wavelength diversity will be available to apply an inverse scattering algorithm. We consider wavelength diversity in this paper to acquire the measured data necessary to estimate a superresolved solution to the inverse scattering problem.

关键词： Metamaterials superresolution scattering inverse scattering algorithm

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