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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

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Multi-modal Denoising Diffusion Pre-training for Whole-Slide image Classification 24

Multi-modal Denoising Diffusion Pre-training for Whole-Slide...

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Lou, Wei Li, Guanbin Wan, Xiang Li, Haofeng The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China

ISBN: (纸本)9798400706868

Whole-slide image (WSI) classification methods play a crucial role in tumor diagnosis. Most of them use hematoxylin and eosin (H&E) stained images, while Immunohistochemistry (IHC) staining provides molecular markers and protein expression information that highlights cancer regions. However, obtaining IHC-stained images requires higher costs in practice. In this work, we propose a multi-modal denoising diffusion pre-training framework that harnesses the advantages of IHC staining to learn visual representations. The framework is trained with the H&E-to-IHC re-staining task and IHC-stained image reconstruction task, which helps capture the structural similarity and staining difference between two image modalities. The trained model can then provide IHC-guided features, by taking only H&E-stained images as inputs. Besides, we build a new class-constraint constrastive loss to achieve the semantic consistency between dual-modal features from our pre-training framework. To integrate with WSI classifiers based on multi-instance learning, we further propose a bag feature augmentation strategy to extend bags with the features extracted by our pre-trained model. Experimental results on three datasets show that our pre-training framework effectively improves WSI classification and surpasses the state-of-the-art pre-training approaches. Code and model are released via https://***/lhaof/MDDP. © 2024 ACM.

关键词： image reconstruction

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DNPM: A Neural Parametric Model for the Synthesis of Facial Geometric Details

DNPM: A Neural Parametric Model for the Synthesis of Facial ...

引用

IEEE International conference on Multimedia and Expo (ICME)

作者： Cai, Haitao Cheng, Baoping Pu, Qiran Zhang, Haocheng Luo, Bin Zhuang, Yixiang Lin, Juncong Chen, Liyan Chen, Xuan Xiamen Univ Sch Informat Xiamen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China China Mobile Hangzhou Informat Technol Co Ltd Hangzhou Zhejiang Peoples R China Xiamen Univ Malaysia Sch Comp & Data Sci Sepang Malaysia

ISBN: (纸本)9798350390155;9798350390162

Parametric 3D models have enabled a wide variety of computer vision and graphics tasks, such as modeling human faces, bodies and hands. In 3D face modeling, 3DMM is the most widely used parametric model, but can't generate fine geometric details solely from identity and expression inputs. To tackle this limitation, we propose a neural parametric model named DNPM for the facial geometric details, which utilizes deep neural network to extract latent codes from facial displacement maps encoding details and wrinkles. Built upon DNPM, a novel 3DMM named Detailed3DMM is proposed, which augments traditional 3DMMs by including the synthesis of facial details only from the identity and expression inputs. Moreover, we show that DNPM and Detailed3DMM can facilitate two downstream applications: speech-driven detailed 3D facial animation and 3D face reconstruction from a degraded image. Extensive experiments have shown the usefulness of DNPM and Detailed3DMM, and the progressiveness of two proposed applications.

关键词： 3DMM Geometric Details 3D Facial reconstruction Speech-Driven 3D Animation

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Focal Aggregation Transformer for Light Field image Super-Resolution 7th

Focal Aggregation Transformer for Light Field Image Super-Re...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Wang, Shunzhou Lu, Yao Xia, Wang Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Peoples R China Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Dept Engn Shenzhen 518172 Peoples R China Beijing Inst Technol Sch Comp Sci Beijing 100081 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

Transformer has achieved significant progress in light field image super-resolution (LFSR) due to its long-range dependency learning ability for inter-intra view feature aggregation. However, locality information of each sub-aperture view is ignored in intra-view and inter-view aggregation with Transformer, hampering the high-quality light field image reconstruction. To this end, we propose a global to local aggregation approach termed Focal Aggregation for LFSR. In particular, Focal Aggregation includes two strategies: inter-view global to local aggregation (InterG2L) and intra-view global to local aggregation (IntraG2L). InterG2L is proposed to obtain complementary information from different views. IntraG2L is developed to extract efficient representations of a single sub-aperture view. InterG2L and IntraG2L are organized in a cascade way so that the global information of the input can be gathered for each sub-aperture image in a coarse to fine aggregation approach. Meanwhile, we also develop a global to local hierarchical feature aggregation approach named HierG2L, which enhances the last hierarchical feature used for light field reconstruction according to the input. Based on the above three global to local aggregation strategies, we construct a focal aggregation transformer (FAT) for LFSR. Experiments are performed on commonly-used LFSR benchmarks. Results demonstrate that FAT achieves superior results compared with other leading methods on synthesized and real data.

关键词： Light field image super-resolution Inter-intra view feature aggregation Hierarchical feature aggregation Transformer

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SALVE: A 3D reconstruction Benchmark of Wounds from Consumer-Grade Videos

SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer...

引用

2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Chierchia, Remi Lebrat, Leo Ahmedt-Aristizabal, David Salvado, Olivier Fookes, Clinton Cruz, Rodrigo Santa Queensland University of Technology Australia Csiro Data61 Australia

ISBN: (纸本)9798331510831

Managing chronic wounds is a global challenge that can be alleviated by the adoption of automatic systems for clinical wound assessment from consumer-grade videos. While 2D image analysis approaches are insufficient for handling the 3D features of wounds, existing approaches utilizing 3D reconstruction methods have not been thor-oughly evaluated. To address this gap, this paper presents a comprehensive study on 3D wound reconstruction from consumer-grade videos. Specifically, we introduce the SALVE dataset, comprising video recordings of realistic wound phantoms captured with different cameras. Using this dataset, we assess the accuracy and precision of state-of-the-art methods for 3D reconstruction, ranging from traditional photogrammetry pipelines to advanced neural rendering approaches. In our experiments, we observe that photogrammetry approaches do not provide smooth surfaces suitable for precise clinical measurements of wounds. Neural rendering approaches show promise in addressing this issue, advancing the use of this technology in wound care practices. We encourage the readers to visit the project page: https://***/SALVE/. © 2025 IEEE.

关键词： 3D reconstruction

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Lung Electrical Impedance Tomography Based on Improved Generative Adversarial Network 6th

Lung Electrical Impedance Tomography Based on Improved Gener...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International conference on Web and Big data (APWeb-WAIM)

作者： Li, Xiuyan Zhang, Ruzhi Wang, Qi Duan, Xiaojie Wang, Jianming Tiangong Univ Tianjin 300387 Peoples R China

ISBN: (纸本)9789819913534;9789819913541

The image reconstruction of electrical impedance tomography (EIT) is highly ill-posed and nonlinear, the reconstructed images tend to have artifacts due to noise in the measurement system. Although deep neural networks have demonstrated great potential to remove artifacts from initial conductivity images, the interpretability and generalization ability of the network is difficult to guarantee. A deep learning structure, namely, a conditional generative adversarial network with an attention mechanism and residual connection (CGAN-AMR), is proposed for EIT image reconstruction. The attention mechanism is utilized in the generator to learn channel dependencies, and residual connection is employed by the discriminator to improve training efficiency. As a result, the accuracy and interpretability of CGAN-AMR are improved compared with CNN and CGAN methods in the EIT imaging task. The imaging results indicate that CGAN-AMR structure can effectively improve the clarity of the reconstructed lung images, the location and boundary of lung lesions are restored accurately.

关键词： Electrical impedance tomography image reconstruction Generative adversarial network Lung disease

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Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment 33rd

Unifying Visual and Semantic Feature Spaces with Diffusion M...

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33rd International conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Zheng, Yuze Li, Zixuan Lv, Xiangxian Liu, Jinxing Wang, Yuqing Meng, Xiangxu Meng, Lei Shandong Univ Sch Software Jinan Peoples R China

ISBN: (纸本)9783031723469;9783031723476

image classification models often demonstrate unstable performance in real-world applications due to variations in image information, driven by differing visual perspectives of subject objects and lighting discrepancies. To mitigate these challenges, existing studies commonly incorporate additional modal information matching the visual data to regularize the model's learning process, enabling the extraction of high-quality visual features from complex image regions. Specifically, in the realm of multimodal learning, cross-modal alignment is recognized as an effective strategy, harmonizing different modal information by learning a domain-consistent latent feature space for visual and semantic features. However, this approach may face limitations due to the heterogeneity between multimodal information, such as differences in feature distribution and structure. To address this issue, we introduce a Multimodal Alignment and reconstruction Network (MARNet), designed to enhance the model's resistance to visual noise. Importantly, MARNet includes a cross-modal diffusion reconstruction module for smoothly and stably blending information across different domains. Experiments conducted on two benchmark datasets, Vireo-Food172 and Ingredient-101, demonstrate that MARNet effectively improves the quality of image information extracted by the model. It is a plug-and-play framework that can be rapidly integrated into various image classification frameworks, boosting model performance.

关键词： image classification Cross-modal alignment Diffusion model

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Exploring data merging methods for a distributed processing system 20

Exploring data merging methods for a distributed processing ...

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20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT)

作者： Konopka, Piotr von Haller, Barthelemy CERN ALICE Expt CH-1211 Geneva 23 Switzerland

The ALICE experiment at the CERN LHC (Large Hadron Collider) is undertaking a major upgrade during the LHC Long Shutdown 2 in 2019-2021, which includes a new computing system called O-2 (Online-Offline). The raw data input from the ALICE detectors will increase a hundredfold, up to 3.5 TB/s. By reconstructing the data online, it will be possible to compress the data stream down to 100 GB/s before storing it permanently. The O-2 software is a message-passing system. It will run on approximately 500 computing nodes performing reconstruction, compression, calibration and quality control of the received data stream. As a direct consequence of having a distributed computing system, locally generated data might be incomplete and could require merging to obtain complete results. This paper presents the O-2 Mergers, the software designed to match and combine partial data into complete objects synchronously to data taking. Based on a detailed study and results of extensive benchmarks, a qualitative and quantitative comparison of different merging strategies considered to reach the final design and implementation of the software is discussed.

关键词： Merging

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Tomato Fruit 3D reconstruction Based on Improved YOLOv8 and MiDaS Depth Estimation 5

Tomato Fruit 3D Reconstruction Based on Improved YOLOv8 and ...

引用

5th International conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

作者： Ye, Rong Shao, Guoqi Gao, Yanjun Li, Tong Gao, Quan Yunnan Agricultural University College of Food Science and Technology Kunming650201 China Yunnan Agricultural University The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province Kunming650201 China Yunnan Agricultural University Big Data College Kunming650201 China

ISBN: (纸本)9798350355253

In order to address the challenges of inaccurate crop phenotypic parameters and unclear crop growth status in crop yield prediction, this study focuses on tomato fruits in greenhouse production environments. It delves into the three-dimensional reconstruction of multi-view images of tomato fruits. This paper introduces an enhanced YOLOv8 tomato fruit recognition model to address the challenge of overlapping fruits, leaves, and stems in complex background environments. By incorporating the CBAM attention mechanism into the model backbone, the recognition mAP@0.5 index improves to 99.3%, a 1.9% increase from the original model. Three-dimensional reconstruction technology is currently a research focus in the field of crop yield prediction. The three-dimensional structure of a crop is a reflection of its growth status. By establishing an accurate three-dimensional crop model, researchers can analyze crop organs to obtain phenotypic parameters. The MiDaS algorithm estimates depth, and the Wonder3D algorithm achieves three-dimensional reconstruction of single-view images. These methods offer an effective approach for reconstructing tomato fruits in three dimensions from multiple viewpoints, with potential applications in tomato yield prediction and the development of harvesting robots. © 2024 IEEE.

关键词： MiDaS multi-view image three-dimensional reconstruction Tomato

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Improved coronary artery calcium (CAC) detection in conventional CT with deep-learning image de-blurring

Improved coronary artery calcium (CAC) detection in conventi...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Wuelker, Christian van der Werf, Niels R. Schnellbaecher, Nikolas D. Greuter, Marcel J. W. Grass, Michael Philips Innovat Technol Hamburg Germany Philips Healthcare Eindhoven Netherlands Univ Med Ctr Groningen Groningen Netherlands

ISBN: (纸本)9781510671553;9781510671546

We investigated the impact of a CNN-based deep-learning (DL) image de-blurring algorithm on coronary artery calcium (CAC) detection performance in conventional CT imaging. Our approach comprises first de-noising the image with a state-of-the-art CNN-based image de-noising algorithm. With improved SNR, it is then possible to sharpen the image with a CNN-based image de-blurring algorithm. We train such networks using natural images, i.e., a large set of diverse photographs. The de-noising strength in the final image can be adjusted by blending back the estimated noise from the first step to the desired degree. To assess the impact of the de-blurring algorithm, we scanned an anthropomorphic phantom containing 100 small calcifications on a CT system using a CAC scoring protocol. data were acquired at clinical and high dose, and subsequently reconstructed with and without the DL de-blurring algorithm, using 25% of the maximum de-noising strength. For each small CAC, detectability was defined as the ability to calculate an Agatston score (at least 3 adjacent voxels exceeding 130 HU). For the high dose scans, CAC detectability increased from 39% for the standard reconstruction to 49% with de-blurring. The same 39% CAC detectability at high dose without de-blurring was obtained with routine dose with de-blurring. In this work, we also show some visual impressions of applying our DL de-blurring method to clinical cardiac data.

关键词： Computed tomography (CT) image quality machine learning deep learning de-blurring de-noising coronary artery calcium clinical task-based metrics

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Joint Bi-Modal Tomographic reconstruction from Observations with Missing data 9

Joint Bi-Modal Tomographic Reconstruction from Observations ...

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9th International Topical Meeting on Neutron Radiography, ITMNR 2022

作者： Leatherland, A. Bazin, N. Aldrich-Smith, G. Boardman, R. Blumensath, T. AWE plc ReadingRG7 4PR United Kingdom University of Southampton SO17 1BJ United Kingdom

A method for tomographic reconstruction of bi-modal data sets using cross modal regularization was developed. The method constrains the Simultaneous Algebraic reconstruction Technique by using clustering-based segmented images and quality controlled fused images, to jointly regularize the reconstruction of structures imaged with different methods. The joint regularized reconstruction method was tested using a synthetic X-Ray and neutron data set with a mix of attenuation driven data starvation. Assuming consistent material boundaries in the different imaging modalities, it was shown that improvements in terms of the Feature Similarity Index Measure are made compared to a traditional SART-TV reconstruction. UK Ministry of Defence ©Crown Owned Copyright 2023/AWE © 2023 Institute of Physics Publishing. All rights reserved.

关键词： image reconstruction

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