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 compu...
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Photoacoustic imaging (PAI) offers significant advantages but faces challenges in data processing and reconstruction. Sparse reconstruction techniques and compressed sensing theory have advanced its development. Regul...
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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 Wo...
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
Compressive imaging (CI) consists of reconstructing images fromincomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measur...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Compressive imaging (CI) consists of reconstructing images fromincomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measurements, with a greater number of measurements typically leading to more accurate reconstructions. Due to their ability to learn data distributions, diffusion models (DM) have emerged as promising techniques for various inverse problems. Mainly, DMs solve inverse problems by conditioning the generation process to the acquired measurements. In this work, we introduce a new approach to improve this conditioning by exploiting synthetic measurements, which come from a synthetic sensing matrix. Synthetic measurements are estimated from real data via a neural network. The combined real and synthetic measurements form an augmented set, which is input into the conditional DM to enhance reconstruction capacity. Computational experiments demonstrate that augmenting measurements with the conditional DM improves performance compared to using only real measurements.
Purpose: Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images fromincompletedata remains a c...
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The proceedings contain 24 papers from the Proceedings of SPIE: imagereconstructionfromincompletedataiii. The topics discussed include: study of polarized light in scattering media using speckle intensity correla...
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The proceedings contain 24 papers from the Proceedings of SPIE: imagereconstructionfromincompletedataiii. The topics discussed include: study of polarized light in scattering media using speckle intensity correlations;image restoration techniques for partially coherent 2-D ladar imaging systems;parallel multiframe blind deconvolution using wavelength diversity;unified imaging theory for x-ray and acoustic computerized tomography;regularized two-step brain activity reconstructionfrom spatiotemporal EEG data;a total-variation-based regularization strategy in magnetic resonance imaging;and dynamic demosaicing and color superresolution of video sequences.
Stable Fast 3D is widely recognized for its remarkable capacity to generate 3D models from a single 2D image in as little as 0.5 seconds. This can be further improved upon by utilizing text-to-image latent diffusion e...
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ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
Stable Fast 3D is widely recognized for its remarkable capacity to generate 3D models from a single 2D image in as little as 0.5 seconds. This can be further improved upon by utilizing text-to-image latent diffusion especially using the inpainting technique in the stable diffusion. The purpose of this work is to improve the quality and fidelity of the generation of 3D models by allowing user-guided customizations during the reconstruction process. Inpainting confronts two significant challenges: incomplete or noisy input data, and visualization differences, by completing unobserved areas and improving input textures. Inpainting enables users to iteratively modify their inputs, and potentially provide more coherent and aesthetically pleasing final 3D models. Experimental results indicate that by utilizing inpainting incoporated with Stable Fast 3D, increases the model precision, while retaining the original speed of model generation. The method proposed in this paper expands the use of 3D reconstruction techniques to other domains including gaming, virtual reality, and product design by providing a solution that is both more interactive and easier to create high-quality 3D assets.
Reconstructing high-quality computed tomography (CT) images from limited-angle projections is a challenging and ill-posed problem, often resulting in severe artifacts and loss of structural details. Traditional analyt...
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ISBN:
(数字)9798331533816
ISBN:
(纸本)9798331533823
Reconstructing high-quality computed tomography (CT) images from limited-angle projections is a challenging and ill-posed problem, often resulting in severe artifacts and loss of structural details. Traditional analytical methods, such as Filtered Back Projection (FBP), struggle with incompletedata, while existing deep learning approaches face limitations in generalization and reliance on extensive paired datasets. To address these challenges, we propose a novel Generative Adversarial Network (GAN)-based framework comprising a U-Net-inspired generator enhanced with residual blocks and selfattention mechanisms, coupled with a PatchGAN discriminator. The generator effectively captures long-range dependencies and structural features critical for artifact removal and reconstruction accuracy, while the PatchGAN discriminator enforces local texture realism. Additionally, a perceptual loss derived from a pretrained VGG network preserves fine anatomical details and high-level semantic consistency. Extensive evaluations on clinical datasets demonstrate the superiority of our method over state-of-the-art techniques. Quantitative metrics, including PSNR, SSIM, and MSE, confirm significant improvements, and qualitative results showcase the effective suppression of artifacts and recovery of fine structural details.
Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at mic...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.
As of 2023, a record 117 million people have been dis-placed worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR's mandate, with 8.7 million residing in...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
As of 2023, a record 117 million people have been dis-placed worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR's mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dan-gerous areas, increasing their vulnerability. [7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, cov-ering 84 km 2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions.
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