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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

computers, Materials & Continua 2022年第10期73卷 533-546页

作者： Mahmoud Ragab Hesham A.Abdushkour Alaa F.Nahhas Wajdi H.Aljedaibi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare *** aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical *** recently developed deep learning(DL)models find a way for accurate lung nodule classification ***,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)*** proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise ***,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet *** addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule ***,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification *** experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various *** experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

关键词： Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions

arXiv

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arXiv 2023年

作者： Sun, Yuqi He, Ruian Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose the first novel interactive framework that utilizes human instructions to edit talking radiance fields to achieve personalized talking face generation. Given a short speech video, we first build an efficient talking radiance field and then apply the latest conditional diffusion model to edit images with human instructions during the optimization. To ensure audio-lip synchronization during the editing process, we propose a progressive dataset updating strategy to ensure successful editing while preventing distortion of lip shapes. We also introduce a lightweight refinement network for complementing image details and achieving controllable detail generation in the final rendered image. Our method enables real-time rendering at up to 30FPS on consumer hardware. Multiple metrics and user verification show that our approach significantly improves rendering quality compared to state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Rendering (computer graphics)

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Fine-Grained Blind Face Inpainting with 3D Face Component Disentanglement

Fine-Grained Blind Face Inpainting with 3D Face Component Di...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Bai Ruian He Weimin Tan Bo Yan Yangle Lin School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Inpainting is a task to restore occlusion or other corruption on images. However, previous works require mask of the occluded area to restore the occluded image, which is inconvenient for application. Blind face inpainting aims to automatically restore the occluded face without position information of the corrupt region. In this paper, we propose a novel fine-grained blind face inpainting framework, combining 3D face components disentanglement with generative network. Canonical face texture and shape disentangled by unsupervised 3D face model is restored separately to get occlusion-free rendered result. Finally, the pixel-to-pixel generative module utilize the occlusion image and the coarse de-occlusion face to get refined inpainted result. We also build up a new dataset called CelebO-3D which consists of occluded face images synthesized with 3D occlusion and rendered by 3D face model. Extensive experiments show that the proposed method is effective and robust in face blind inpainting both in synthesized and real images. Extensive evaluations and comparison with previous methods also show our superior effectiveness and lightweight architecture.

关键词： Solid modeling Three-dimensional displays Shape Speech enhancement Signal processing Acoustics Image restoration

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The relationship between brain atrophy and brain network connection in Alzheimer's disease 16

The relationship between brain atrophy and brain network con...

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16th ICME International Conference on Complex Medical Engineering, CME 2022

作者： Wang, Tong Wang, Bin Li, Zhifeng Ma, Yunxiao Taiyuan University of Technology Brain Science and Intelligence Computing Research Center Department of Information and Computer Taiyuan030024 China Beijing Institute of Technology School of Life Science Beijing100089 China

ISBN: (纸本)9781665496995

Purpose: Researchers found that the connectome structure of the brain spreads atrophy of cortical and subcortical gray matter in a series of dementia diseases. Many researchers study the transmission mode of brain atrophy through brain networks. However, most of the evidence related to atrophy and brain network connectivity is at the pathological level, and the evidence of correlation at the network level is *** and Methods: A total of 378 subjects were included in our study, including 173 AD (88 women, mean age 75.3±7.6 years), and 205 normal controls (NC) (106 women, mean age 76.1 ±4.8 years). All data of the subjects were from T1 weighted data of the public dataset ADNI. In this paper, we first use the VBM method to analyze the gray matter difference between AD and NC. Then, the AAL template is used to divide the t map into brain regions. The correlation between node atrophy and neighbor atrophy is used as the verification ***: It is found that the node atrophy of AD is significantly related to their neighbor atrophy. As a control, when the random matrix is used as the brain network connection, there is no significant correlation between the node atrophy of AD and their neighbor ***: This study found that brain atrophy in AD patients was significantly related to its network connection, indicating that the propagation of brain atrophy in AD can be explained by network connection. © 2022 IEEE.

关键词： Brain

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Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection

Motion Matters: Difference-based Multi-scale Learning for In...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ruian He Shili Zhou Ri Cheng Yuqi Sun Weimin Tan Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. 1

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On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) 38

On Statistical Rates and Provably Efficient Criteria of Late...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jerry Yao-Chieh Wu, Weimin Li, Zhuoru Pi, Sophia Song, Zhao Liu, Han Center for Foundation Models and Generative AI Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94720 United States

We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study the universal approximation and sample complexity of the DiTs score function, as well as the distribution recovery property of the initial data. Specifically, under mild data assumptions, we derive an approximation error bound for the score network of latent DiTs, which is sub-linear in the latent space dimension. Additionally, we derive the corresponding sample complexity bound and show that the data distribution generated from the estimated score function converges toward a proximate area of the original one. Computationally, we characterize the hardness of both forward inference and backward computation of latent DiTs, assuming the Strong Exponential Time Hypothesis (SETH). For forward inference, we identify efficient criteria for all possible latent DiTs inference algorithms and showcase our theory by pushing the efficiency toward almost-linear time inference. For backward computation, we leverage the low-rank structure within the gradient computation of DiTs training for possible algorithmic speedup. Specifically, we show that such speedup achieves almost-linear time latent DiTs training by casting the DiTs gradient as a series of chained low-rank approximations with bounded error. Under the low-dimensional assumption, we show that the statistical rates and the computational efficiency are all dominated by the dimension of the subspace, suggesting that latent DiTs have the potential to bypass the challenges associated with the high dimensionality of initial data. © 2024 Neural information processing systems foundation. All rights reserved.

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Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

arXiv

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arXiv 2023年

作者： Cheng, Ri Jiang, Xuhao He, Ruian Zhou, Shili Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In this paper, we develop an Uncertainty-Guided Spatial Pruning (UGSP) architecture to skip redundant computation for efficient frame interpolation dynamically. Specifically, pixels with low uncertainty indicate easy regions, where the calculation can be reduced without bringing undesirable visual results. Therefore, we utilize uncertainty-generated mask labels to guide our UGSP in properly locating the easy region. Furthermore, we propose a self-contrast training strategy that leverages an auxiliary non-pruning branch to improve the performance of our UGSP. Extensive experiments show that UGSP maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method achieves state-of-the-art performance with lower FLOPs on multiple benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation

arXiv

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arXiv 2024年

作者： Mou, Xinyi Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China School of Computer Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Social media has emerged as a cornerstone of social movements, wielding significant influence in driving societal change. Simulating the response of the public and forecasting the potential impact has become increasingly important. However, existing methods for simulating such phenomena encounter challenges concerning their efficacy and efficiency in capturing the behaviors of social movement participants. In this paper, we introduce a hybrid framework HiSim for social media user simulation, wherein users are categorized into two types. Core users are driven by Large Language Models, while numerous ordinary users are modeled by deductive agent-based models. We further construct a Twitter-like environment to replicate their response dynamics following trigger events. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for evaluation and conduct comprehensive experiments across real-world datasets. Experimental results demonstrate the effectiveness and flexibility of our method 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Computational methods

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