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Automated CT-based 3D surgical planning for total hip replacement: A pilot study

International Congress Series 2003年第C期1256卷 389-394页

作者： Nakamoto, Masahiko Sato, Yoshinobu Sugano, Nobuhiko Sasama, Toshihiko Nishii, Takashi Sik Pak, Pyong Akazawa, Kenzo Tada, Yukio Yoshikawa, Hideki Tamura, Shinichi Division of Interdisciplinary Image Analysis Graduate School of Medicine Osaka University 2-2 Yamadaoka Suita Osaka 565-0871 Japan Department of Orthopaedic Surgery Graduate School of Medicine Osaka University Japan Graduate School of Information Science Technology Osaka University Japan Department of Computer Systems Engineering Kobe University Japan

At the preoperative planning stage of CT-based computer-assisted total hip replacement, a surgeon determines the parameters such as size, position, and orientations of the implants based on interactive visualization of 3D models of the implants and hip joint bone. However, the parameters depend on surgeon's visual assessment of spatial relationship between the hip joint bone and implants. Our objective is to investigate objective criteria for determination of the optimal parameters and formulate an automated determination procedure based on the criteria. We objectified expertise in the preoperative planning of an experienced surgeon into quantitative evaluations and geometrical constraints, then formulated the automated procedure as an optimization problem. The automated planning system was applied to three cases of patient data sets and compared with an experienced surgeon. In the preliminary results, the planning parameters determined by the system were generally acceptable as a pilot experiment. © 2003, Elsevier science B.V. All rights reserved.

关键词： Geometric constraint Optimal implant alignment Quantitative evaluation

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An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection 6

An Improved Transfer Learning-Based Model with Data Augmenta...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Prayogo, Rizal Dwi Hamid, Nur Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350344349

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-The-Art brain tumor detection. © 2024 IEEE.

关键词： Tumors

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Smart Data Augmentation for Multiclass Brain Tumor Detection with Imbalanced Data 8

Smart Data Augmentation for Multiclass Brain Tumor Detection...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Prayogo, Rizal Dwi Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350368970

Dealing with class imbalance is a significant issue in classification tasks that often leads to lower prediction performance. Many data augmentation methods have been suggested to tackle this problem, but their effectiveness can vary depending on the dataset. Utilizing augmentation methods can improve the learning and generalization abilities of the proposed convolutional neural network (CNN) model. However, it is challenging to find a single data augmentation method that can effectively address class imbalance problems across all scenarios. In this paper, we present a technique known as smart data augmentation (SDA) that is aimed at effectively improving classification performance by enhancing imbalanced data. SDA involves an equation that can be used to create an augmentation technique, combining different sampling methods for addressing multiclass imbalance and offering easy customization options. We perform the data balancing strategy including undersampling, oversampling, and SDA-based sampling. We evaluate our proposed framework using a CNN model to improve the detection of brain tumors in imbalanced data. Our experiments demonstrate that SDA-based sampling improves brain tumor detection performance by 3.546% in accuracy, 3.310% in precision, 3.432% in recall, and 3.371 % in Fl-score compared to the imbalanced data scenario. The findings suggest that utilizing SDA together with CNN can improve the classification performance of brain tumor detection with imbalanced data. © 2024 IEEE.

关键词： Brain tumor detection class imbalance problem convolutional neural network smart data augmentation

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An Improved Image Segmentation Method for Melasma Severity Assessment 22

An Improved Image Segmentation Method for Melasma Severity A...

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2017 22nd International Conference on Digital Signal Processing (DSP)

作者： Liang, Yunfeng Lin, Zhiping Sun, Lei Ser, Wee Lin, Feng Thng, Steven Tien Guan Interdisciplinary Graduate School Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Information and Electronic Beijing Institute of Technology Beijing PR China School of Computer Science and Engineering Nanyang Technological University Singapore National Skin Center Singapore

ISBN: (纸本)9781538618950

Melasma is a widely spread skin pigmentation disease and accurate assessments of the disease severity is crucial during its treatment. Recently, several computerized methods have been developed to overcome the shortcomings of the conventional clinical assessment method. As a key step in algorithm, image segmentation has extensive impacts on the accuracy of the assessment. Currently, the optimal hybrid thresholding (oHybrid) segmentation method that adaptively combines both local and global thresholding methods has obtained reasonable results in solving the melasma assessment problem. Nevertheless, the distance measure adopted in the oHybrid method is empirically selected and its influence is not well discussed. In this paper, a generalized distance measure is introduced and applied to improve the hybrid thresholding image segmentation method. The proposed method is tested on a data set of melasma patients to determine the lesion severity and it shows the best overall performance among the methods compared.

关键词： Image segmentation Lesions Correlation Skin Optimized production technology Market research Diseases

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Thermal annealing effects on β-Ga 2 O 3 /diamond (111) heteroepitaxial structures fabricated by radio frequency magnetron sputtering

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Materials Letters 2025年

作者： Wang Yixin Sreenath Mylo Valappil Yuki Katamune Itsuki Misono Ikegami Yuto Hiroshi Naragino Phongsaphak Sittimart Abdelrahman Zkria Tsuyoshi Yoshitake Interdisciplinary Graduate School of Engineering Sciences Kyushu University Kasuga Fukuoka 816-8580 Japan Department of Electrical and Electronic Engineering Kyushu Institute of Technology 1-1 Sensuicho Tobata Kitakyushu Fukuoka 804-8550 Japan Department of Electrical and Computer Engineering TGGS King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Department of Physics Faculty of Science Aswan University Aswan 81528 Egypt

Heteroepitaxial integration of β-Ga 2 O 3 on diamond (111) has been achieved using radio frequency magnetron sputtering. The β-Ga 2 O 3 epilayer exhibited a high level of in-plane stacking with {-201} parallel to diamond {111}, as confirmed by X-ray diffraction analyses. Pole figure stereographic projections effectively concluded the inoccupancy of (101) texture in the β-Ga 2 O 3 film. The heterojunction preserved its mechanical integration, crystallinity, and heteroepitaxial characteristics up to 900 °C thermal annealing for 40 min. However, annealing at 1000 °C led to structural degradation, attributed to thermal graphitization at the C–O interface. Notably, post-fabrication thermal annealing at 900 °C significantly reduced the oxygen vacancy density in the β-Ga 2 O 3 epilayer from 24.15 % to 15.07 %.

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Hough transform-based clock skew measurement by dynamically locating the region of offset majority

Hough transform-based clock skew measurement by dynamically ...

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作者： Oka Saputra, Komang Teng, Wei-Chung Nara, Takaaki Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Department of Electrical and Computer Engineering Udayana University Bali80361 Indonesia Graduate School of Interdisciplinary Information Studies University of Tokyo Tokyo113-0033 Japan

A network-based remote host clock skew measurement involves collecting the offsets, the differences between sending and receiving times, of packets from the host within a period of time. Although the variant and immeasurable delay in each packet prevents the measurer from getting the real clock offset, the local minimum delays and the majority of delays delineate the clock offset shifts, and are used by existing approaches to estimate the skew. However, events during skew measurement like time synchronization and rerouting caused by switching network interface or base transceiver station may break the trend into multi-segment patterns. Although the skew in each segment is theoretically of the same value, the skew derived from the whole offset-set usually differs with an error of unpredictable scale. In this work, a method called dynamic region of offset majority locating (DROML) is developed to detect multi-segment cases, and to precisely estimate the skew. DROML is designed to work in realtime, and it uses a modified version of the HT-based method [8] both to measure the skew of one segment and to detect the break between adjacent segments. In the evaluation section, the modified HT-based method is compared with the original method and with a linear programming algorithm (LPA) on accumulated-time and short-term measurement. The fluctuation of the modified method in the short-term experiment is 0.6 ppm (parts per million), which is obviously less than the 1.23 ppm and 1.45 ppm from the other two methods. DROML, when estimating a four-segment case, is able to output a skew of only 0.22 ppm error, compared with the result of the normal case. Copyright © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Linear programming

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling 38

Structured Multi-Track Accompaniment Arrangement via Style P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

arXiv

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arXiv 2022年

作者： Zhou, Xinyu Liu, Chang Zhao, Jun The School of Computer Science and Engineering Nanyang Technological University Singapore ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore

The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. Due to privacy concerns and the limited computation resources on mobile devices, we incorporate FL into MAR systems of the Metaverse to train a model cooperatively. Besides, to balance the trade-off between energy, execution latency and model accuracy, thereby accommodating different demands and application scenarios, we formulate an optimization problem to minimize a weighted combination of total energy consumption, completion time and model accuracy. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, CPU frequency and video frame resolution for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm has better performance (in terms of energy consumption, completion time and model accuracy) under different weight parameters compared to existing benchmarks. © 2022, CC BY.

关键词： Frequency division multiple access

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