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arXiv 2023年

作者： Bianco, Michele Giri, Sambit K. Prelogović, David Chen, Tianyue Mertens, Florent G. Tolley, Emma Mesinger, Andrei Kneib, Jean-Paul Observatoire de Sauverny Versoix1290 Switzerland Astronomy Centre Department of Physics & Astronomy University of Sussex Pevensey III Building Falmer BrightonBN1 9QH United Kingdom Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa56126 Italy LERMA Observatoire de Paris PSL Research University CNRS Sorbonne Université ParisF-75014 France

The upcoming Square Kilometre Array Observatory (SKAO) will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination which pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift z = 7 to 11. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2’s independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with AUC = 95% is possible for 8 ion > (10 cMpc)3 at z > 9, for follow-up studies with infrared/optical telescopes to detect these sources. © 2023, CC BY-NC-SA.

关键词： Deep learning

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A CT-based method to compute femur remodelling after total hip arthroplasty

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COMPUTER methods IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2021年第4期9卷 428-437页

作者： Iuppariello, L. Esposito, Luca Gargiulo, P. Gislason, M. K. Jonsson, H. Sarno, A. Cristofolini, L. Bifulco, P. AORN Santobono Pausilipon Childrens Hosp Rehabil Unit Naples Italy Univ Campania Luigi Vanvitelli Dept Engn Aversa CE Brazil Univ Federico II Naples Dept Struct Engn & Architecture DIST Naples Italy Univ Iceland Landspitali Univ Hosp Dept Sci Inst Biomed & Neural Engn Reykjavik Iceland Univ Iceland Landspitali Univ Hosp Dept Orthopaed Sci Reykjavik Iceland Univ Federico II Naples Sect Napoli INFN Naples Italy Univ Federico II Naples Dept Phys Naples Italy Univ Bologna Dept Ind Engn Bologna Italy Ist Clin Sci Maugeri SpA Soc Benefit Dept Biomed Signal Proc Lab Clin Res Telese Terme BN Italy

Bone remodelling after total hip arthroplasty has been largely observed and investigated. Most studies rely on projective images and only few obtain 3D information with limited spatial resolution. This study proposes a method to provide quantitative, 3D high-resolution data about femur bone density variations, by means of CT volume processing. This would offer a tool for further research and clinical studies. Five patients subjected to primary, cementless total hip arthroplasty were considered. Calibrated CT volumes were acquired before, just after surgery and 1 yr later. Bone remodelling hinders accurate alignment of femur volumes acquired after a year;instead, prosthesis stem remains unchanged. Thus, after metal artefact reduction, prosthesis was segmented, and stem-based accurate alignment was obtained. A test to exclude prosthesis migration was performed by considering specific femur anatomical landmarks. Bone density error due to artefact reduction and realignment was estimated. Quantitative differences in bone mineral density were computed for each voxel, providing a resolution of about 1 mm. Preliminary results showed that the femur underwent consistent remodelling after a year. Widespread bone density losses appeared in those areas where strain-adaptive remodelling is normally expected, particularly about the calcar. Conversely, distal areas with clear stem-bone contact showed considerable density gains.

关键词： Total hip arthroplasty femur bone remodelling CT image processing prosthesis rigid realignment

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A Stacked Generalization Chest-X-Ray-Based Framework for Mispositioned Medical Tubes and Catheters Detection

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BIOMEDICAL signal processing AND CONTROL 2023年第Part1期79卷 000-000页

作者： Elaanba, Abdelfettah Ridouani, Mohammed Hassouni, Larbi Hassan II Univ RITM Lab CED Engn Sci Casablanca Morocco

Tubes and catheters are medical devices introduced into the human body to help ill patients in critical health conditions. However, several positioning errors occur during or after the placement of such devices (Endotracheal tubes mispositioned in 10 to 20% of intubations). In addition, the delay of X-ray diagnosis after surgery can cause serious complications. Such delays are caused by the hospitals' resourcelessness or due to workload in intensive care units. The X-rays images availability (Most used diagnosis modality in intensive care units, 40% to 50%) and the presence of tubes in those images (lines are present on 33% of X-ray images) present a fertile ground to feed DCNNs training on tube error detection tasks and reduce complications. However, training and tuning one DCNN learner to resolve tube detection is time-consuming. Therefore, we propose a custom stacked generalization framework to combine wake learners with a proposed meta learner neural network architecture to resolve tube error detection tasks. The proposed framework AUC (93.84%) outperforms other related work methods with the input size of (380pixel*380pixel). Furthermore, we demonstrated the sensibility of stacked generalization to the number of base learners. Moreover, we validated the utility of input cross-validation used to form level1-metadata for the stacked generalization. Our framework can be adapted to be integrated with a CAD (computer aid decision system) for tubes error detection. The CAD can detect errors immediately after patient screening and notify radiologists to prioritize diagnosis of cases with positioning errors to adjust tubes and reduce risks significantly.

关键词： Stacked Generalization Endotracheal tube Central venous catheter Nasogastric tube Chest X-ray image Convolutional networks Deep learning

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Deep emotion recognition based on audio-visual correlation

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IET COMPUTER VISION 2020年第7期14卷 517-527页

作者： Hajarolasvadi, Noushin Demirel, Hasan Eastern Mediterranean Univ Dept Elect & Elect Engn 10 Via Mersin TR-99628 Cyprus Turkey

Human emotion recognition is studied by means of unimodal channels over the last decade. However, efforts continue to answer tempting questions about how variant modalities can complement each other. This study proposes a multimodal approach using three-dimensional (3D) convolutional neural networks (CNNs) to model human emotion through a modality-referenced system while investigating the solution to such questions. The proposed modality-referenced system selects the input data based on one of the modalities regarded as reference or master. The other modality which is referred to as a slave simply adjusts or attunes itself with the master in the temporal domain. In this context, the authors developed three multimodal emotion recognition system, namely, video-referenced system, audio-referenced system, and the audio-visual-referenced system to explore the congruence impact of audio and video modalities on each other. Two pipelines of 3D CNN architectures are employed where k-means clustering is used in the master pipeline and the slave pipeline adapts itself in a temporal sense. The outputs of the two pipelines are fused to improve recognition performance. In addition, canonical correlation analysis and t-distributed stochastic neighbour embedding is used validating the experiments. Results show that temporal alignment of the data between two modalities improves the recognition performance significantly.

关键词： deep emotion recognition video modalities audio–visual-referenced system slave pipeline multimodal approach variant modalities k-means clustering multimodal emotion recognition system emotion recognition video-referenced system 3D CNN architectures pattern clustering audio–visual correlation audio-referenced system unimodal channels t-distributed stochastic neighbour embedding video signal processing convolutional neural nets modality-referenced system image fusion human emotion recognition master pipeline canonical correlation analysis correlation methods recognition performance audio-visual systems three-dimensional convolutional neural networks neural net architecture temporal data alignment temporal domain

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SSGD: SPARSITY-PROMOTING stochastic GRADIENT DESCENT ALGORITHM FOR UNBIASED DNN PRUNING

SSGD: SPARSITY-PROMOTING STOCHASTIC GRADIENT DESCENT ALGORIT...

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IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lee, Ching-Hua Fedorov, Igor Rao, Bhaskar D. Garudadri, Harinath Univ Calif San Diego Dept ECE La Jolla CA 92093 USA ARM ML Res San Jose CA USA

ISBN: (纸本)9781509066315

While deep neural networks (DNNs) have achieved state-of-the-art results in many fields, they are typically over-parameterized. Parameter redundancy, in turn, leads to inefficiency. Sparse signal recovery (SSR) techniques, on the other hand, find compact solutions to overcomplete linear problems. Therefore, a logical step is to draw the connection between SSR and DNNs. In this paper, we explore the application of iterative reweighting methods popular in SSR to learning efficient DNNs. By efficient, we mean sparse networks that require less computation and storage than the original, dense network. We propose a reweighting framework to learn sparse connections within a given architecture without biasing the optimization process, by utilizing the affine scaling transformation strategy. The resulting algorithm, referred to as Sparsity-promoting stochastic Gradient Descent (SSGD), has simple gradient-based updates which can be easily implemented in existing deep learning libraries. We demonstrate the sparsification ability of SSGD on image classification tasks and show that it outperforms existing methods on the MNIST and CIFAR-10 datasets.

关键词： Deep learning sparse signal recovery network pruning iterative reweighting affine scaling

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Edge-AI on Wearable Devices: Myocardial Infarction Detection with Spectrogram and 1D-CNN

Edge-AI on Wearable Devices: Myocardial Infarction Detection...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Maria Gragnaniello Fiona Balbi Gabriella Martellotta Alessandro Borghese Vincenzo Romano Marrazzo Luca Maresca Giovanni Breglio Andrea Irace Michele Riccio Department of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The Electrocardiogram (ECG) provides a detailed representation of the heart's electrical activity, emerging as a crucial resource for continuous cardiac health monitoring. Recent advances in Artificial Intelligence (AI) techniques have revolutionized ECG signal processing, creating new possibilities for every-day health monitoring. The exploitation of these AI technologies has driven a growing interest in wearable devices where the challenge is to implement these functionalities on limited memory resource hardware. In this scenario, the Edge Computing paradigm, where computation occurs near the data source rather than in a remote data center, emerges as a promising solution. This article proposes an efficient approach for Myocardial Infarction (MI) detection based on Deep Learning (DL) methods using spectrogram and a 1D Convolutional neural Network (1D-CNN). The aim is to strike a balance between computational efficiency and accuracy enabling practical application on wearable devices. In the presented work, a study on the impact of the spectrogram parameters and results on the 1D-CNN was conducted. The final training phase yielded a remarkable accuracy of 95.94%, showcasing the efficacy of the proposed approach. Notably, the trained model was successfully deployed on a 32-bit microcontroller featuring an ARM Cortex-M4 architecture, underscoring the feasibility of real-world implementation for embedded systems in healthcare applications.

关键词： Training Accuracy image edge detection Electrocardiography Myocardium Real-time systems Wearable devices

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A Convolutional-Attentional neural Framework for Structure-Aware Performance-Score Synchronization

arXiv

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arXiv 2022年

作者： Agrawal, Ruchit Wolff, Daniel Dixon, Simon Centre for Digital Music Queen Mary University of London United Kingdom Institute for Research and Coordination in Acoustics/Music Paris France

Performance-score synchronization is an integral task in signal processing, which entails generating an accurate mapping between an audio recording of a performance and the corresponding musical score. Traditional synchronization methods compute alignment using knowledge-driven and stochastic approaches, and are typically unable to generalize well to different domains and modalities. We present a novel data-driven method for structure-aware performance-score synchronization. We propose a convolutional-attentional architecture trained with a custom loss based on time-series divergence. We conduct experiments for the audio-to-MIDI and audio-to-image alignment tasks pertained to different score modalities. We validate the effectiveness of our method via ablation studies and comparisons with state-of-the-art alignment approaches. We demonstrate that our approach outperforms previous synchronization methods for a variety of test settings across score modalities and acoustic conditions. Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches. © 2022, CC BY.

关键词： Alignment

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An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis

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signal image AND VIDEO processing 2020年第1期14卷 205-213页

作者： Rohil, Mukesh Kumar Gupta, Neetika Yadav, Prakash Birla Inst Technol & Sci Dept Comp Sci & Informat Syst Pilani Campus Pilani Rajasthan India

No-reference image quality assessment (NR-IQA) uses only the test image for its quality assessment, and as video is essentially comprised of image frames with additional temporal dimension, video quality assessment (VQA) requires a thorough understanding of image quality assessment metrics and models. Therefore, in order to identify features that deteriorate video quality, a fundamental analysis of spatial and temporal artifacts with respect to individual video frames needs to be performed. Existing IQA and VQA metrics are primarily for capturing few distortions and hence may not be good for all types of images and videos. In this paper, we propose an NR-IQA model by combining existing three methods (namely NIQE, BRISQUE and BLiiNDS-ii) using multi-linear regression. We also present a holistic no-reference video quality assessment (NR-VQA) model by exploring quantification of certain distortions like ringing, frame difference, blocking, clipping and contrast in video frames. For the proposed NR-IQA model, the results represent improved performance as compared to the state-of-the-art methods and it requires very low fraction of samples for training to provide a consistent accuracy over different training-to-testing ratios. The performance of NR-VQA model is examined using a simple neural network model to attain high value of goodness of fit.

关键词： No-reference image quality assessment No-reference video quality assessment Spatial artifacts Temporal artifacts

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The Pitfalls of Simplicity Bias in neural Networks 34

The Pitfalls of Simplicity Bias in Neural Networks

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34th Conference on neural Information processing Systems (NeurIPS)

作者： Shah, Harshay Tamuly, Kaustav Raghunathan, Aditi Jain, Prateek Netrapalli, Praneeth Microsoft Res Redmond WA 98052 USA Stanford Univ Stanford CA USA

ISBN: (纸本)9781713829546

Several works have proposed Simplicity Bias (SB)-the tendency of standard training procedures such as stochastic Gradient Descent (SGD) to find simple models-to justify why neural networks generalize well [1, 49, 74]. However, the precise notion of simplicity remains vague. Furthermore, previous settings [67, 24] that use SB to justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks-a widely observed phenomenon in practice [71, 36]. We attempt to reconcile SB and the superior standard generalization of neural networks with the non-robustness observed in practice by introducing piecewise-linear and image-based datasets, which (a) incorporate a precise notion of simplicity, (b) comprise multiple predictive features with varying levels of simplicity, and (c) capture the non-robustness of neural networks trained on real data. Through theoretical analysis and targeted experiments on these datasets, we make four observations: (i) SB of SGD and variants can be extreme: neural networks can exclusively rely on the simplest feature and remain invariant to all predictive complex features. (ii) The extreme aspect of SB could explain why seemingly benign distribution shifts and small adversarial perturbations significantly degrade model performance. (iii) Contrary to conventional wisdom, SB can also hurt generalization on the same data distribution, as SB persists even when the simplest feature has less predictive power than the more complex features. (iv) Common approaches to improve generalization and robustness-ensembles and adversarial training-can fail in mitigating SB and its pitfalls. Given the role of SB in training neural networks, we hope that the proposed datasets and methods serve as an effective testbed to evaluate novel algorithmic approaches aimed at avoiding the pitfalls of SB.

关键词： stochastic systems

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Tiny-BDN: An Efficient and Compact Barcode Detection Network

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2020年第4期14卷 688-699页

作者： Jia, Jun Zhai, Guangtao Ren, Ping Zhang, Jiahe Gao, Zhongpai Min, Xiongkuo Yang, Xiaokang Inst Image Commun & Network Engn Shanghai Key Lab Digital Media Proc & Transmiss Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Shanghai Lighten Intelligent Technol Co Ltd Shanghai Peoples R China

This paper presents a novel approach for accurate barcodes detection in real and challenging environments using compact deep neural networks. Our approach is based on Convolutional neural Network (CNN) and neural network compression, which can detect the four vertexes coordinates of a barcode accurately and quickly. Our approach consists of four stages: (i) feature extraction by a base network, (ii) region proposal network (RPN) training, (iii) barcode classification and coordinates regression, and (iv) weights pruning and recoding. The model is trained in the first three stages and then compressed in the fourth stage to reduce the size of the trained model. In order to remove the effect of geometric distortion during barcode decoding, we add a distortion removal module to the end of the trained model. In experiments, we validate our approach on a challenging large-scale dataset. Compared with previous methods, our method can locate the coordinates of a barcode accurately and quickly and enhance decoding rate through distortion removal. In addition, the storage and memory overheads of our model are reduced through model compression, which shows great potential in industrial applications.

关键词： Barcode detection challenging environment convolutional neural network model compression

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