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image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization

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SCIENTIFIC REPORTS 2023年第1期13卷 1-11页

作者： Mateus, Pedro Volmer, Leroy Wee, Leonard Aerts, Hugo J. W. L. Hoebers, Frank Dekker, Andre Bermejo, Inigo Maastricht Univ GROW Sch Oncol & Reprod Dept Radiat Oncol Maastro Med Ctr Maastricht Netherlands Maastricht Univ Clin Data Sci Maastricht Netherlands Harvard Med Sch Artificial Intelligence Med AIM Program Mass Gen Brigham Boston MA USA Maastricht Univ Med Ctr Dept Radiol & Nucl Med Maastricht Netherlands Harvard Med Sch Dana Farber Canc Inst Brigham & Womens Hosp Dept Radiat Oncol Boston MA USA Harvard Med Sch Dept Radiol Dana Farber Canc Inst Brigham & Womens Hosp Boston MA USA

In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such studies, which has impeded closer examination of the models and, in turn, their implementation in healthcare. On the other hand, the performance of these models is highly dependent on decisions on architecture and image pre-processing. In this work, we assess the reproducibility of three studies that use CNNs for head and neck cancer outcome prediction by attempting to reproduce the published results. In addition, we propose a new network structure and assess the impact of image pre-processing and model selection criteria on performance. We used two publicly available datasets: one with 298 patients for training and validation and another with 137 patients from a different institute for testing. All three studies failed to report elements required to reproduce their results thoroughly, mainly the image pre-processing steps and the random seed. Our model either outperforms or achieves similar performance to the existing models with considerably fewer parameters. We also observed that the pre-processing efforts significantly impact the model's performance and that some model selection criteria may lead to suboptimal models. Although there have been improvements in the reproducibility of deep learning models, our work suggests that wider implementation of reporting standards is required to avoid a reproducibility crisis.

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Detection of bee diseases with a hybrid deep learning method

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2021年第3期36卷 1716-1731页

作者： Metlek, Sedat Kayaalp, Kiyas Burdur Mehmet Akif Ersoy Univ Teknik Bilimler Meslek Yuksekokulu Elekt Otomasyon Bolumu TR-15100 Burdur Turkey Isparta Uygulamali Bilimler Univ Teknol Fak Bilgisayar Muhendisligi TR-32080 Isparta Turkey

Bees are one of the oldest living species in the world, having a major impact on the development of living species. The continuity of plants at the bottom of the food chain is directly related to the pollination of bees. Bees are a global insurance because of this characteristic. For this reason, it is very important to check the health status of bees. Depending on the technology developed nowadays, it is possible to control the health status of bees remotely with real-time image processing applications. In the study, feature extraction methods, which are the strengths of deep learning, were executed from two different arms and aggressive changes in images were detected. In the classification process;Instead of Softmax classifier based on probability calculation, multi-layer feedback artificial neural network (MLFB-ANN) model has been used. The success of the designed system has also been compared with the Softmax classifier. As a result of experimental studies, 93.07% success rate can be achieved with Softmax classifier for six different bee diseases on the same data set, while 95.04% success rate has been obtained with the developed system. In this study, a hybrid method based on deep learning methods was proposed for the classification of bee diseases and successful results were obtained.

关键词： Bee diseases deep learning convolution neural networks artifical neural network classification

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SDR-Based Metal Classification Using Spectrogram images from Micro-Doppler Signatures

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第3期28卷 22-29页

作者： Liaquat, Salman Butt, Faran Awais Nasir, Faryal Aurooj Naqvi, Ijaz Haider Mahyuddin, Nor Muzlifah Muqaibel, Ali Hussein Alawsh, Saleh Ahmed Univ Sains Malaysia Elect & Elect Engn George Town Penang Malaysia Univ Management & Technol Lahore Pakistan Inst Space Technol Artificial Intelligence Islamabad Pakistan Lahore Univ Management Sci Sch Sci & Engn Lahore Pakistan Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Commun Syst & Sensing Dhahran Saudi Arabia

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro- Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.

关键词： Vibrations Accuracy Aluminum Feature extraction Nonhomogeneous media Sensors Convolutional neural networks Copper Object recognition Spectrogram Metallic materials

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TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR ...

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

作者： Rong Li Shijie Li Xieyuanli Chen Teli Ma Juergen Gall Junwei Liang HKUST(GZ) China University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany HKUST China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the "many-to-one" problem caused by the range image ’s limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the "many-to-one" issue. We evaluated the approach on two benchmarks and demonstrated that the plugin post-processing technique is generic and can be applied to various networks.

关键词： Training Laser radar Three-dimensional displays image resolution Semantic segmentation Face recognition neural networks

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Adaptive Spiking neural networks with Hybrid Coding

arXiv

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arXiv 2024年

作者： He, Huaxu School of Computer and Information Engineering Henan University China

The Spiking neural Network (SNN), due to its unique spiking-driven nature, is a more energy-efficient and effective neural network compared to artificial neural networks (ANNs). The encoding method directly influences the overall performance of the network, and currently, direct encoding is primarily used for directly trained SNNs. When working with static image datasets, direct encoding inputs the same feature map at every time step, failing to fully exploit the spatiotemporal properties of SNNs. While temporal encoding converts input data into spike trains with spatiotemporal characteristics, traditional SNNs utilize the same neurons when processing input data across different time steps, limiting their ability to integrate and utilize spatiotemporal information *** address this, this paper employs temporal encoding and proposes the Adaptive Spiking neural Network (ASNN), enhancing the utilization of temporal encoding in conventional SNNs. Additionally, temporal encoding is less frequently used because short time steps can lead to significant loss of input data information, often necessitating a higher number of time steps in practical applications. However, training large SNNs with long time steps is challenging due to hardware constraints. To overcome this, this paper introduces a hybrid encoding approach that not only reduces the required time steps for training but also continues to improve the overall network ***, significant improvements in classification performance are observed on both Spikformer and Spiking ResNet *** code is available at https://***/hhx0320/ASNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality image Fusion

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IEEE TRANSACTIONS ON image processing 2021年 30卷 1261-1274页

作者： Liu, Risheng Liu, Jinyuan Jiang, Zhiying Fan, Xin Luo, Zhongxuan Dalian Univ Technol DUT RT Int Sch Informat Sci & Engn Dalian 116620 Peoples R China Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Guilin Univ Elect Technol Inst Artificial Intelligence Guilin 541004 Peoples R China

image fusion plays a critical role in a variety of vision and learning applications. Current fusion approaches are designed to characterize source images, focusing on a certain type of fusion task while limited in a wide scenario. Moreover, other fusion strategies (i.e., weighted averaging, choose-max) cannot undertake the challenging fusion tasks, which furthermore leads to undesirable artifacts facilely emerged in their fused results. In this paper, we propose a generic image fusion method with a bilevel optimization paradigm, targeting on multi-modality image fusion tasks. Corresponding alternation optimization is conducted on certain components decoupled from source images. Via adaptive integration weight maps, we are able to get the flexible fusion strategy across multi-modality images. We successfully applied it to three types of image fusion tasks, including infrared and visible, computed tomography and magnetic resonance imaging, and magnetic resonance imaging and single-photon emission computed tomography image fusion. Results highlight the performance and versatility of our approach from both quantitative and qualitative aspects.

关键词： image fusion Task analysis Transforms Optimization Magnetic resonance imaging Dictionaries Medical diagnostic imaging image fusion bilevel model deep prior neural networks

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Unleashing Energy-Efficiency: neural Architecture Search without Training for Spiking neural networks on Loihi Chip

Unleashing Energy-Efficiency: Neural Architecture Search wit...

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IEEE International Symposium on Quality Electronic Design

作者： Shiya Liu Yang Yi Bradley Department of Electrical and Computing Engineering Virginia Tech Blacksburg Virginia USA

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

Spiking neural networks (SNNs) offer energy-efficient computation due to their high-sparsity activation and event-driven nature. However, existing SNN designs often utilize suboptimal artificial neural network (ANN)-like architectures for binary sequence processing. Moreover, improving accuracy often leads to higher computational complexity, making it difficult to deploy SNNs on resource-constrained devices. Furthermore, SNN architectures tailored for GPUs may not fully exploit the energy-efficient capabilities of SNN models. To address these limitations, we present a novel neural architecture search (NAS) algorithm that merges recent advancements in ANNs and focuses on enhancing SNN architectures specifically for the Loihi chip. The Loihi chip is a neuromorphic computing chip designed to emulate the brain’s neural networks, with particular strength in event-driven SNNs, making it an energy-efficient alternative to GPUs. Our algorithm efficiently selects an optimal architecture by leveraging gradients induced at initialization across diverse data samples, eliminating the requirement for training. We propose to design a search space that aligns with the chip’s capabilities, taking into account its support for integer-only inference and the lack of advanced operators such as backward and shortcut connections. Experimental results on two image classification benchmarks demonstrate the superiority of our SNN models, which achieve comparable accuracy to state-of-the-art architectures while significantly reducing energy consumption per image and minimizing model size. Our approach paves the way for energy-efficient SNN designs on the Loihi chip, unlocking the full potential of SNNs for real-world applications.

关键词： Training Measurement Energy consumption Neuromorphic engineering Computer architecture Benchmark testing Energy efficiency

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A High Efficiency and Reconfigurable Neuromorphic Engine with Parallel – Pipeline Pooling Scheme

A High Efficiency and Reconfigurable Neuromorphic Engine wit...

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China Semiconductor Technology International Conference (CSTIC)

作者： Yue Cheng Chenhao Tang Longyue Zheng Weiming Ma Jiahui Zuo Jinxu Liu Liangyao Deng Yaolei Guo Fei Wang Yongpeng Cheng Yitao Ma College of Integrated Circuits Zhejiang University Hangzhou China Zhejiang ICsprout Semiconductor Co. Ltd Hangzhou China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China

ISBN: (数字)9798331513351

ISBN: (纸本)9798331513368

This paper developed a very high-energy-efficiency neuromorphic chip is proposed and demonstrated with 55nm CMOS technology which is free-expandible to the suitable scales to meet the requirement of various AI applications. A novel compact even-driven near-memory MAC module is proposed based on binary spiking neural networks (BSNNs) to minimize both memory volume and memory access. Additionally, a parallel-pipeline pooling scheme is adopted using a multi-threaded post-convolutional max-pooling computation, to further reduce the data access for intermediate results during convolution processing. The prototype chip with four MAC modules was designed and fabricated under 55nm-CMOS technology for image recognition. The chip results verified a high-energy efficiency of 21.08TOPS/W @1.2V/ 100MHz with a handwritten-digit recognition accuracy of 96.92%.

关键词： Power demand Neuromorphics Scalability Memory management Pipelines Prototypes Spiking neural networks System-on-chip artificial intelligence Engines

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RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

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Research on Deep Learning-Based Liver Tumor image Segmentation Techniques

Research on Deep Learning-Based Liver Tumor Image Segmentati...

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Information Technology,Big Data and artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Bohao Zeng Xingqiong Chen Jinghan Mao Yingmei Ran School of Mathematics and Statistics Chongqing Jiaotong University China Yunyang County People’s Hospital China Fengdu General Hospital China Fengdu CH-Medicine Hospital China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Early detection and accurate diagnosis of liver tumors are crucial for patient prognosis. In recent years, deep learning techniques have made significant advancements in the field of medical image processing, particularly in liver tumor image segmentation. This paper reviews the latest research progress on deep learning-based liver tumor image segmentation techniques, focusing on the application of various deep learning models, including Convolutional neural networks (CNNs), U-Net, and its variants. By comparing the performance and efficiency of different methods, the advantages and disadvantages of each model in liver tumor segmentation tasks are discussed. Additionally, this paper proposes a new data preprocessing method called Color Preprocessing (CP), which effectively improves the segmentation accuracy of processed data under the same methods. Finally, future research directions are envisioned, highlighting the importance of deep learning technologies in improving the accuracy of liver tumor image segmentation and their potential clinical applications.

关键词： Deep learning image segmentation Accuracy Reviews image color analysis Liver Focusing Prognostics and health management Information technology Tumors

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