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Multi-modal Diffusion Network with Controllable Variability for Medical image Segmentation

Multi-modal Diffusion Network with Controllable Variability ...

引用

ieee International conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoyu Wang Zheyu Zhang Yueyi Zhang Jing Zhang Yunwei Ou Xiaoyan Sun University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In diffusion-based medical segmentation models, stochastic sampling is commonly used to generate multiple masks. However, the inherent variability in diffusion models can lead to significant biases in some masks, resulting in the fused mask deviating from the true mask. In this study, we propose a novel multi-modal diffusion segmentation network (MMDSN) with controllable variability, specifically designed to address the issue of variability in diffusion models. MMDSN achieves multi-modal conditional control through medical text annotations, thereby enhancing consistency of visual semantic representation and establishing a correspondence between vision and language for diffusion models. Additionally, MMDSN constrains the uncertainty distributions of multiple timesteps within the latent Gaussian space, controlling the variability at each denoising timestep. Extensive experiments on the Qata-Covid19 and MosMed datasets demonstrate that our proposed method surpasses existing state-of-the-art diffusion networks, producing a high-quality, controllable segmentation map with just a single reverse diffusion step and one sampling.

关键词： image segmentation visualization Uncertainty Semantics Noise reduction Stochastic processes Contrastive learning Gaussian distribution Diffusion models Biomedical imaging

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HIGH-LEVEL visual MASKING OF image COMPRESSION ARTEFACTS

HIGH-LEVEL VISUAL MASKING OF IMAGE COMPRESSION ARTEFACTS

引用

ieee International conference on image processing (ICIP)

作者： Le Moan, Steven Pedersen, Marius Chetouani, Aladine Massey Univ Dept Mech & Elect Engn Palmerston North New Zealand Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway Univ Orleans PRISME Lab Orleans France

ISBN: (纸本)9781728163956

We present the results of a subjective experiment where we measured detection thresholds for 2 degrees-wide noise targets placed in 23 different natural scenes. Unlike previous studies on visual masking, we focus particularly on dissociating cases of low-level and high-level masking. That is, cases where the target is not perceived predominantly due to limits of either early or late vision. To that end, we exploit the change blindness paradigm and analyse detection rates, times and primed subjective ratings of target visibility. Our results are of significance for developing advanced models of human vision for signal quality/fidelity assessment, particularly in the context of compression.

关键词： image Quality Assessment Perception visual Memory Change Blindness

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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yijun Wang Yong Wang Chendong Xu Shuai Yao Qisong Wu Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD.

关键词： Computer vision Accuracy Spaceborne radar Transfer learning Speech recognition Radar imaging Feature extraction Transformers Human activity recognition Speech processing

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Multi-Contrast MR image Synthesis with a Brownian Diffusion Model

Multi-Contrast MR Image Synthesis with a Brownian Diffusion ...

引用

ieee Signal processing and communications Applications (SIU)

作者： Bilal Kabas Fuat Arslan Valiyeh Ansarian Nezhad Tolga Çukur Elektrik ve Elektronik Mühendisliği Bölümü Bilkent Üniversitesi Ankara Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye Sinirbilim Programı Mühendislik ve Fen Bilimleri Enstitüsü Bilkent Üniversitesi Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Magnetic Resonance Imaging (MRI) plays a significant role in medical diagnostics. However, prolonged scan times may hinder its widespread applicability in clinical settings. To mitigate this challenge, certain contrasts within multi-contrast MRI protocols can be excluded, and these target contrasts can then be synthesized from the acquired set of source contrasts retrospectively. Recently introduced generative adversarial and diffusion based MRI synthesis models yield enhanced performance against classical methods, yet there can still benefit from technical improvements. In this study, we propose a Brownian diffusion-based multi-contrast MR image synthesis model. Existing diffusion models synthesize images starting from a Gaussian noise sample, so guidance from the source contrast images are weakened. Conditional denoising diffusion models employs a weak conditioning during reverse process within the denoising network that may result in suboptimal sample generation due to poor convergence to target distribution. Capitalizing Brownian diffusion, the proposed model instead incorporates stronger guidance toward the target contrast distribution via a refined diffusion process. Experimental results suggest that our method attains higher performance in noise reduction and capture of tissue structural details over existing methods.

关键词： Protocols image synthesis Navigation Magnetic resonance imaging Noise reduction Diffusion models Imputation

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People Detection and Tracking Using a Fisheye Camera Network

People Detection and Tracking Using a Fisheye Camera Network

引用

ieee visual communications and image processing (VCIP)

作者： Tsaipei Wang Chih-Hao Liao Li-Hsuan Hsieh Arvin Wen Tsui Hsin-Chien Huang National Yang Ming Chiao Tung University Hsinchu Taiwan ROC Industrial Technology Research Institute Hsinchu Taiwan ROC

ISBN: (纸本)9781728173221

In this paper we study techniques for accurate detection, localization, and tracking of multiple people in an indoor scene covered by multiple top-view fisheye cameras. This is a rarely studied setting within the topic of multi-camera object tracking. The experimental results on test videos exhibit good performance for practical use. We also propose methods to account for occlusion by scene objects at different stages of the algorithm that lead to improved results.

关键词： Location awareness visual communication image processing Computational modeling Graphics processing units Cameras Real-time systems

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A Learnable image Compression Scheme for Synthetic Aperture Sonar imagery

A Learnable Image Compression Scheme for Synthetic Aperture ...

引用

OCEANS conference

作者： Gerg, Isaac D. Monga, Vishal Penn State Univ Appl Res Lab University Pk PA 16802 USA Penn State Univ Sch EECS University Pk PA 16802 USA

ISBN: (纸本)9780692935590

Synthetic aperture sonar (SAS) is an imaging modality which produces high and constant resolution images of the seafloor. These sonars are often mounted to a unmanned underwater vehicle (UUV) to autonomously collect imagery of a prescribed survey area. While a survey is underway, UUV communications back to the operator are often limited due to the use of a low-bandwidth acoustic communications (ACOMMS) channel. Because of this, high-quality SAS imagery is rarely sent over this link due to the lack of an efficient compression scheme to send such information. Creating an efficient SAS image compression scheme provides at least two operational benefits: (1) image chips beamformed and tagged by onboard processing algorithms can be quickly communicated to operators while a survey is ongoing, and (2) cooperative UUVs can exchange salient image chips among themselves to reconcile position ambiguity and obtain a shared reference frame. In this work we propose a learned image compression scheme for SAS imagery using deep neural networks (DNNs). DNNs have already been applied to the image compression problem but almost exclusively for optical imagery. We highlight some important differences between SAS imagery and optical imagery which prevents the simple application of off-the-shelf (OTS) methods like JPEG and WebP to SAS imagery. We propose an image compression scheme which specifically addresses the domain-specific properties of SAS imagery to obtain useful image compression performance on a real-world SAS dataset. We show that we can reduce the bitrate by up to thirty-five percent while still maintaining the same perceptual image quality as OTS codecs.

关键词： Synthetic aperture sonar (SAS) image Compression deep learning unmanned underwater vehicles acoustic communications (ACOMMS)

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A Multiscale Dual-Attention based Convolutional Neural Network for Ship Classification in SAR image

A Multiscale Dual-Attention based Convolutional Neural Netwo...

引用

2021 ieee International conference on Signal processing, communications and Computing, ICSPCC 2021

作者： Zhou, Gaoyu Zhang, Gong Fang, Zheng Dai, Qijun Ministry of Education Nanjing University of Aeronautics and Astronautics Key Lab of Radar Imaging and Microwave Photonics Nanjing China

ISBN: (纸本)9781665429184

Classical ship target classification algorithms in synthetic aperture radar (SAR) images mainly rely on manual extraction of simple physical features to distinguish different ship types. A novel multiscale dual-Attention (MDA) based convolutional neural network (CNN) is proposed in this paper to highlight the deep distinguishable features extracted from SAR images for classification. First, we apply the data enhancement method to expand the limited labeled sample set to alleviate the defect of unbalanced dataset. Second, the squeeze-And-excitation(SE) block is adopted to learn the initial weight of the feature maps to mitigate the negative effects of large similarity between different ship classes. Third, the weighted feature maps are sent into two parallel self-Attention blocks, and the semantic dependence on spatial dimension and channel dimension is modeled respectively. Feature fusion is carried out to further improve feature discrimination ability. The results of ablation experiments and comparative experiments on the open SAR ship dataset show that this method can mine deep discriminant features effectively, and further improve the classification accuracy of ships in SAR images compared with baseline method and other latest methods. © 2021 ieee.

关键词： Ships

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Scalable Privacy in Multi-Task image Compression

Scalable Privacy in Multi-Task Image Compression

引用

ieee visual communications and image processing (VCIP)

作者： Saeed Ranjbar Alvar Ivan V. Bajić School of Engineering Science Simon Fraser University Burnaby BC Canada

ISBN: (纸本)9781728173221

Learning-based compression systems have shown great potential for multi-task inference from their latent-space representation of the input image. In such systems, the decoder is supposed to be able to perform various analyses of the input image, such as object detection or segmentation, besides decoding the image. At the same time, privacy concerns around visual ana-lytics have grown in response to the increasing capabilities of such systems to reveal private information. In this paper, we propose a method to make latent-space inference more privacy-friendly using mutual information-based criteria. In particular, we show how organizing and compressing the latent representation of the image according to task-specific mutual information can make the model maintain high analytics accuracy while becoming less able to reconstruct the input image and thereby reveal private information.

关键词： Privacy visualization image segmentation image coding visual communication Object detection Multitasking

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Finding Channel-wise Tensor Subspaces in GANs

Finding Channel-wise Tensor Subspaces in GANs

引用

Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Chao Zhang Lanlan Feng Ke Wu Ce Zhu Hongwei Guo Yipeng Liu Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Information and Communications Engineering University of Electronic Science and Technology of China Chengdu China School of Engineering Honghe University Mengzi China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Generative Adversarial Networks (GANs) can map a latent vector sampled from a random distribution to a photorealistic image. Previous methods attempt to leverage the semantic features embedded in the latent space to enable local editing but encounter challenges in accurately disentangling these features. In this paper, we analyze the redundant characteristics inherent in the deconvolution mechanism and explore the high-order redundant information of feature maps to find channel-wise tensor subspaces in GANs. We propose an architecture-agnostic unsupervised approach, where tensor subspace clustering is used for the first time to disentangle the interpretable semantic features in GANs. Each cluster is regarded as a semantic mask by calculating its mean. The relationship between the latent vector with the selected cluster is established through network gradients to identify the interpretable directions. Note that the proposed method circumvents the demand of manually drawing regions of interest and only computes gradients in the shallow layers rather than the entire network. Experimental results on three GAN-based architectures show that our method can well disentangle the semantic features in GANs and achieves more precise local editing on facial images compared to the state-of-the-art methods.

关键词： Training Tensors Deconvolution Semantics Data visualization Computer architecture Generative adversarial networks Data processing Vectors Photorealistic images

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PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label image Classification

PatchCT: Aligning Patch Set and Label Set with Conditional T...

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International conference on Computer Vision (ICCV)

作者： Miaoge Li Dongsheng Wang Xinyang Liu Zequn Zeng Ruiying Lu Bo Chen Mingyuan Zhou National Key Laboratory of Radar Signal Processing Xidian University Xi’an China McCombs School of Business The University of Texas at Austin Austin TX USA

Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods.

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