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Frequency Filtering Network for Undersampled MRI Reconstruction 2

Frequency Filtering Network for Undersampled MRI Reconstruct...

引用

2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Chen, Xinren Liu, Zhaohui Xiong, Dongping Ouyang, Lijun Zhang, Xiaozhi University of South China School of Computing/Software Hengyang China University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350331417

Magnetic resonance imaging (MRI) is an imaging technology widely used in medical clinical diagnosis. Nevertheless, it always takes a long imaging time to obtain the high spatial resolution MR image. Image reconstruction has been playing a crucial role for accelerated MRI. Recently, deep neural networks show potential to significantly speed up MRI. However, these deep learning methods usually learn the feature information in the spatial domain. Although some works have attempted to utilize the frequency information from the k-space to reconstruct MR images, they still suffer from the inability to consider the global information. To address these limitations, in this work, we propose a new residual filter channel attention network (RFCAN) for undersampled MRI reconstruction. Specifically, the proposed model consists of a novel filter channel attention (FCA) in the frequency domain and the multiscale cascade convolution in the spatial domain to learn both the dual-domain features. This model employs the filtering instead of the traditional self-attention mechanism in Transformer to learn k-space information. It can avoid the limitations of the Transformer window to better learn the global information. We also introduce a frequency channel attention to fine-tune the filtering results to better balance the global and local information. Experiment results show that our model consistently outperforms other state-of-the-art methods and can achieve high-quality MRI reconstruction. © 2023 IEEE.

关键词： channel attention deep learning Frequency filtering network MRI reconstruction

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Fast feature selection algorithm of EEG data based on convolutional neural network 6

Fast feature selection algorithm of EEG data based on convol...

引用

6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Liu, Bin Cao, Liming Fang, Tianke Xiamen University of Technology School of Software Engineering Xiamen China Mapua University School of Information Technology Manila Philippines Xiamen University of Technology School of Computing and Information Engineering Xiamen China

ISBN: (纸本)9798331516550

Electroencephalography (EEG) data is invaluable for investigating EEG physiology, neuroscience and clinical medicine. However, EEG signals are characterized by temporal nature, high dimensionality, and noise, which make it difficult for traditional feature selection methods for EEG data to extract useful information effectively with high computational effort. To solve this problem, this paper proposed a fast feature selection algorithm for multichannel EEG data based on a convolutional neural network (FFSEEG-GNN). Specifically, the algorithm pre-processes the multichannel EEG data, then extracts the waveform features and builds a CUDA architecture using GPUs to convolutional neural network operations. Finally, the most representative subset of features is selected in order of feature importance and feature extraction is performed. In addition, the simulation comparison experiment proves that the algorithm achieves 92.60% in terms of precision rate. Therefore, the program has accurate feature selection capability, which is expected to play an essential role in clinical medicine. © 2024 IEEE.

关键词： Convolutional neural networks

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Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks

Think Before You Act: A Two-Stage Framework for Mitigating G...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Yunqi Li, Songda Deng, Chunyuan Wang, Luyi Zhao, Hui Software Engineering Institute East China Normal University China Georgia Institute of Technology United States Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9798891761148

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability. © 2024 Association for Computational Linguistics.

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MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy 2

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

引用

2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Xie, Xinyu Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Electrical Engineering Hengyang China University of South China School of Computing/Software Hengyang China

ISBN: (纸本)9798350331417

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality. © 2023 IEEE.

关键词： Detail-aware Fine-grained information Fourier Transform Multimodal medical image fusion

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Activation Sparsity Opportunities for Compressing General Large Language Models

Activation Sparsity Opportunities for Compressing General La...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Nobel Dhar Bobin Deng Md Romyull Islam Kazi Fahim Ahmad Nasif Liang Zhao Kun Suo College of Computing and Software Engineering Kennesaw State University

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices’ independent capabilities, alleviate the server’s burden, and lower the response time. Owing to these tremendous potentials, many big tech companies have been actively promoting edge LLM evolution and released several lightweight Small Language Models (SLMs) to bridge this gap. However, SLMs currently only work well on limited real-world applications. We still have huge motivations to deploy more powerful (larger-scale) AI models on edge devices and enhance their smartness level. Unlike the conventional approaches for AI model compression, we investigate from activation sparsity. The activation sparsity method is orthogonal and combinable with existing techniques to maximize compression rate while maintaining great accuracy. According to statistics of open-source LLMs, their Feed-Forward Network (FFN) components typically comprise a large proportion of parameters (around $\tfrac{2}{3}$). This internal feature ensures that our FFN optimizations would have a better chance of achieving effective compression. Moreover, our findings are beneficial to general LLMs and are not restricted to ReLU-based *** work systematically investigates the tradeoff between enforcing activation sparsity and perplexity (accuracy) on state-of-the-art LLMs. Our empirical analysis demonstrates that we can obtain around 50% of main memory and computing reductions for critical FFN components with negligible accuracy degradation. This extra 50% sparsity does not naturally exist in the current LLMs, which require tuning LLMs’ activation outputs by injecting zero-enforcing thresholds. To obtain the benefits of activation sparsity, we provide a guideline for the system architect for LLM prediction and prefetching. Moreover, we further verified the predictability of activation patterns in recent LLMs. The success prediction allows the system to prefetch the necessary weights whi

关键词： Accuracy Prefetching Large language models Computational modeling Companies Transformers User experience Time factors Tuning Guidelines

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Target Permutation for Feature Significance and Applications in Neural Networks 23

Target Permutation for Feature Significance and Applications...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Biswas, Sanad Grundlingh, Nina Boardman, Jonathan White, Joseph Le, Linh College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States Equifax Inc. AtlantaGA30309 United States

ISBN: (纸本)9798350374889

Statistical techniques like generalized linear models have always been the indispensable tools for understanding relationships in data. However, over the last two decades, growth in data size and complexity has fueled the rise of powerful 'black box' machine learning models (i.e., Deep Learning). Such models lack transparent mechanisms to evaluate the importance and contributions of individual features. This may be unacceptable ethically and/or legally. Moreover, the inability to precisely assess contributions from features complicates model validation and understanding. In this paper, we propose a permutation test for feature importance in differentiable models with a focus on neural networks. Unlike existing permutation-based methods, ours shuffles the target instead of the inputs. This change allows for simultaneous testing of all features and does not require the assumption of independence among inputs that is prevalent in current work in this area. Through extensive experiments, we empirically demonstrate that this permutation test can reveal highly nonlinear associations, is robust to multicollinearity among features, and can be used to filter unnecessary inputs, while preserving or improving models' predictive performance in both classification and regression. © 2024 IEEE.

关键词： Adversarial machine learning

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Boundary-Aware Periodicity-based Sparsification Strategy for Ultra-Long Time Series Forecasting 24

Boundary-Aware Periodicity-based Sparsification Strategy for...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Bao, Yiying Zhou, Hao Peng, Chao Xu, Chenyang Shi, Shuo Cai, Kecheng East China Normal University Shanghai China Software Engineering Institute Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9798400706868

In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model's ability to infer long time series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL).This method effectively captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for improved prediction accuracy. In the paper, we investigate several commonly used benchmark datasets and demonstrate that the proposed method can yield comparable performance across them. © 2024 ACM.

关键词： Weather forecasting

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Magnetic resonance image reconstruction based on two-stage progressive network 3

Magnetic resonance image reconstruction based on two-stage p...

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3rd International Conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Shen, Lingyun Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

ISBN: (纸本)9798350359961

In clinical applications, medical image reconstruction is crucial for extracting complementary information and restoring image quality. However, existing deep learning-based reconstruction methods suffer from the following two problems: first, they ignore the frequency-domain information specific to magnetic resonance imaging, and second, most of the traditional networks are single-end-To-end networks, which lack effective attention to feature information. We suggest a unique two-stage progressive network (TSPNet) to solve these issues. Our approach employs two lightweight Unets as subnetworks for progressive recovery, and the network parts are carefully designed. During the feature extraction phase, we creatively create a Dual Domain Feature Extraction Module (DDFEM) to effectively extract rich features. Subsequently, we create a hybrid attention module (HAM) to effectively process and integrate the retrieved data. Experimental results show that our model outperforms other state-of-The-Art methods and reconstructs images with stronger texture information and superior visual quality. © 2023 IEEE.

关键词： Convolutional neural network deep learning Fourier Transform Medical Image Reconstruction

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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Frequency Domain-based Matching and Integration Network for Multi-contrast MRI Reconstruction 2

Frequency Domain-based Matching and Integration Network for ...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Wang, Lijuan Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Computing/Software Hengyang China University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350331417

Multi-contrast magnetic resonance (MR) images are crucial for diagnosing diseases and analysis in clinical practice, yet their acquisition often entails long scanning procedures. Recently, exploiting shared information among multi-contrast MR images yields favorable results in their reconstruction. However, most studies simply concatenate the multi-contrast information without effective matching and fusion mechanisms to mitigate the impact of redundant features. Furthermore, the feature extraction backbone networks tend to lose high-frequency details like textures during downsampling. To promote the development of this research field, we develop a matching and fusion framework for multi-contrast MR reconstruction and design a feature extractor that explores frequency-domain information. Firstly, our feature extractor captures both long-range and short-range dependencies in the target and reference images, acquiring high- and low-frequency feature information. Then, we introduce a progressive feature matching mechanism to identify features in the reference image that resemble those in the target. Additionally, we propose an innovative fusion strategy that leverages non-local Fourier self-attention and cross-attention mechanisms to precisely combine the semantic feature from multi-contrast MR images. Extensive experiments validate the superior performance of our method in multi-contrast MR image reconstruction, surpassing other state-of-the-art methods. © 2023 IEEE.

关键词： Feature fusion Feature matching Frequency domain Multi-contrast MRI reconstruction

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