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arXiv 2022年

作者： Tomar, Nikhil Kumar Srivastava, Abhishek Bagci, Ulas Jha, Debesh School of Informatics and Computer Science Indira Gandhi National Open University India Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Machine and Hybrid Intelligence Lab Department of Radiology Feinberg School of Medicine Northwestern University United States

The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and minimize the variation among endoscopists. In this study, we introduce a novel deep learning architecture, named MKDCNet, for automatic polyp segmentation robust to significant changes in polyp data distribution. MKDCNet is simply an encoder-decoder neural network that uses the pre-trained ResNet50 as the encoder and novel multiple kernel dilated convolution (MKDC) block that expands the field of view to learn more robust and heterogeneous representation. Extensive experiments on four publicly available polyp datasets and cell nuclei dataset show that the proposed MKDCNet outperforms the state-of-the-art methods when trained and tested on the same dataset as well when tested on unseen polyp datasets from different distributions. With rich results, we demonstrated the robustness of the proposed architecture. From an efficiency perspective, our algorithm can process at (≈ 45) frames per second on RTX 3090 GPU. MKDCNet can be a strong benchmark for building real-time systems for clinical colonoscopies. The code of the proposed MKDCNet is available at https://***/nikhilroxtomar/MKDCNet. © 2022, CC BY.

关键词： Convolution

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

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Correction to: DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression (Neural Computing and Applications, (2024), 36, 29, (18363-18376), 10.1007/s00521-024-10130-4)

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Neural Computing and Applications 2025年

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

In this article, the Eq. (2) and Eq. (3) were incorrectly displayed and it should have been displayed as given below (Formula presented.) (Formula presented.) where n is the total number of key-value pairs in the sequence The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Self-Ensembling Depth Completion Via Density-Aware Consistency

SSRN

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SSRN 2023年

作者： Zhang, Xuanmeng Zheng, Zhedong Jiang, Minyue Ye, Xiaoqing The ReLER Laboratory Australian Artificial Intelligence Institute University of Technology SydneyNSW2007 Australia The Sea-NExT Joint Lab The Department of Computer Science School of Computing National University of Singapore Singapore117417 Singapore The Department of Computer Vision Technology Baidu Inc. Beijing100085 China

Depth completion can predict a dense depth map by taking a sparse depth map and the aligned RGB image as input, but the acquisition of ground truth annotations is labor-intensive and non-scalable. Therefore, we resort to semi-supervised learning, where we only need to annotate a few images and leverage massive unlabeled data without ground truth labels to facilitate model learning. In this paper, we propose SEED, a SElf-Ensembling Depth completion framework to enhance the generalization of the model on unlabeled data. Specifically, SEED contains a pair of the teacher and student models, which are given high-density and low-density sparse depth maps as input respectively. The main idea underpinning SEED is to enforce the density-aware consistency by encouraging consistent prediction across different-density input depth maps. One empirical challenge is that the pseudodepth labels produced by the teacher model inevitably contain wrong depth values, which would mislead the convergence of the student model. To resist the noisy labels, we propose an automatic method to measure the reliability of the generated pseudo-depth labels adaptively. By leveraging the discrepancy of prediction distributions, we model the pixel-wise uncertainty map as the prediction variance and rectify the training process from noisy labels explicitly. To our knowledge, we are among the early semi-supervised attempts on the depth completion task. Extensive experiments on both outdoor and indoor datasets demonstrate that SEED consistently improves the performance of the baseline model by a large margin and even is on par with several fully-supervised methods. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Learning Inverse Dynamics of Multi-segmented Larval Crawling Neuromechanics

Learning Inverse Dynamics of Multi-segmented Larval Crawling...

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Smart World Congress (SWC), IEEE

作者： Weihao Tang Hao Zhang Zhefeng Gong Nenggan Zheng Qiushi Academy for Advanced Studies College of Computer Science and Technology Zhejiang University Hangzhou China Department of neurology of the fourth Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China Qiushi Academy for Advanced Studies the State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Zhejiang University Hangzhou China Bengbu University Bengbu China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Neuromechanical modeling that bridges neural activity and locomotion patterns is essential for studying locomotion control. The Drosophila larva, with its diverse softbodied locomotion, serves as an ideal subject for biomimetic research. Constructing a neuromechanical model of Drosophila larvae presents two primary challenges: efficiently simulating the larva’s soft, multi-segmented body and controlling locomotion within a large action space by actuating muscle groups. This work presents a three-dimensional neuromechanical model of the larva and introduces an inverse dynamics learning method for locomotion control. Our approach efficiently simulates the larva’s multi-segmented, deformable body by integrating computational modules for soft tissues, muscles, and friction. We employ a parameterized controller to reproduce peristaltic waves and use a hybrid artificial neural network architecture to learn inverse dynamics from partially observed states. Balancing biological interpretability with modeling complexity, this model advances the understanding of brain-body-environment interactions in Drosophila larvae, contributing to nature-inspired intelligent systems research.

关键词： Training Solid modeling Recurrent neural networks Biological system modeling Computational modeling Friction Muscles Aerospace electronics Brain modeling Feature extraction

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Continuous Decoding of Movement Trajectory During Unimanual Movement Using Bilateral Motor Cortex Signals 23

Continuous Decoding of Movement Trajectory During Unimanual ...

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Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering

作者： Dongrong Lai Liangliang Chen Weihuang Chen Xiaoxiao Meng Yaoyao Hao Kedi Xu Department of Biomedical Engineering Qiushi Academy for Advanced Studies (QAAS) Zhejiang University China College of Computer Science and Technology Zhejiang University China College of Foreign Languages Zhejiang University China The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Zhejiang University China Qiushi Academy for Advanced Studies (QAAS) Department of Biomedical Engineering Zhejiang University China

ISBN: (纸本)9798400708343

Brain-computer interfaces (BCIs) provide a passway to connect the devices and the living brains by transforming the cortical signals to control commands to the devices. The ability of the contralateral primary motor cortex (M1) to represent sequentially changing kinematics has been proposed in previous intracortical BCI papers. However, it's not clear whether the ipsilateral M1 could be used to decode continuous trajectories like the contralateral one and whether the bilateral brain regions had similar levels of decoding performance. To that end, we recorded the single-unit activities from bilateral M1 during the center-out task performed by a monkey and predicted the 2-dimensional (2D) positions by using partial least squares (PLS). Our results revealed that the decoder with neurons recorded from ipsilateral M1 was able to decode continuous positions. When using the overall dataset, bilateral hemispheres showed an even contribution to the prediction. In addition, we found that the decoder using contralateral neurons outperformed that using ipsilateral neurons, which could be attributed to the stronger correlation of neuron pairs within contralateral M1 versus within ipsilateral M1. Our findings support that the movement kinematics are bihemispherically encoded in M1 and that the underlying structure within contralateral M1 may contribute to the outperformance in the prediction. These results improve our understanding of motor control in M1 and implicate the design of cutting-edge BCIs with a more precise prediction strategy.

关键词： bilateral hemispheres brain-computer interface (BCI) motor control neuronal connectivity primary motor cortex (M1)

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CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

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arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

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Novel ground reaction force-based parameters for monitoring rehabilitation in tibial fractures

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Gait & Posture 2024年 113卷 256-257页

作者： Christian Wolff Elke Warmerdam Tim Dahmen Tim Pohlemann Philipp Slusallek Bergita Ganse German Research Center for Artificial Intelligence Agents and Simulated Reality Saarbrücken Germany Saarland University Saarbrücken Graduate School of Computer Science Saarbrücken Germany Saarland University Werner Siemens-Endowed Chair for Innovative Implant Development Fracture Healing- Departments and Institutes of Surgery Homburg Germany Hochschule Aalen Chair for Computer Vision and Machine Learning Aalen Germany Saarland University Department of Trauma- Hand and Reconstructive Surgery- Departments and Institutes of Surgery Homburg Germany

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Understanding the learning mechanism of convolutional neural network applied to fluorescence spectra 4

Understanding the learning mechanism of convolutional neural...

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AI and Optical Data sciences IV 2023

作者： Venturini, Francesca Michelucci, Umberto Sperti, Michela Gucciardi, Arnaud Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Lucerne University of Applied Sciences and Arts Computer Science Department Lucerne Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9781510659810

The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232 © 2023 SPIE.

关键词： Olive oil

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A 64-channel Fully Implantable Brain-computer Microsystem with Custom Microelectrode Array

A 64-channel Fully Implantable Brain-computer Microsystem wi...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Yaoyao Hao Leixiao Han Youwei Jin Qin Chen Chenbin Yu Bowen Yu Penghai Li Jingzhuo Han Yueming Wang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents the development of a 64-channel fully implantable neural signal recording and processing system. The system is designed with hermetically sealed, commercial off-the-shelf components to enable neural signal acquisition, wireless transmitting and wireless charging. Additionally, an artificial neural network was implemented on the hardware to create a standalone, comprehensive brain signal decoding system. A silicon-based microelectrode array, fabricated with MEMS process and assembled into 3D floating form by stacking planar silicon probes, was attached to the system to sense neural signals. The whole system was militarized packaged into a medical-scale titanium case and embedded onto skull for brain-computer interface (BCI) applications. The study demonstrates the feasibility and potential of this advanced technology for neural signal recording and processing in a fully implantable system.

关键词： Micromechanical devices Wireless communication Microelectrodes Wireless sensor networks Three-dimensional displays Array signal processing Titanium Hardware Recording Decoding

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