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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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Microstructure,optical,and photoluminescence properties ofβ-Ga_(2)O_(3)films prepared by pulsed laser deposition under different oxygen partial pressures

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Chinese Physics B 2021年第2期30卷 578-583页

作者： Rui-Rui Cui Jun Zhang Zi-Jiang Luo Xiang Guo Zhao Ding Chao-Yong Deng Power Semiconductor Device Reliability Center of the Ministry of Education Department of Electronic ScienceCollege of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang 550014China

Theβ-Ga_(2)O_(3)films are prepared on polished Al_(2)O_(3)(0001)substrates by pulsed laser deposition at different oxygen partial *** influence of oxygen partial pressure on crystal structure,surface morphology,thickness,optical properties,and photoluminescence properties are studied by x-ray diffraction(XRD),atomic force microscope(AFM),scanning electron microscope(SEM),spectrophotometer,and *** results of x-ray diffraction and atomic force microscope indicate that with the decrease of oxygen pressure,the full width at half maximum(FWHM)and grain size *** the increase of oxygen pressure,the thickness of the films first increases and then *** room-temperature UV-visible(UV-Vis)absorption spectra show that the bandgap of theβ-Ga_(2)O_(3)film increases from4.76 e V to 4.91 e V as oxygen pressure *** temperature photoluminescence spectra reveal that the emission band can be divided into four Gaussian bands centered at about 310 nm(~4.0 e V),360 nm(~3.44 e V),445 nm(~2.79 e V),and 467 nm(~2.66 e V),*** addition,the total photoluminescence intensity decreases with oxygen pressure increasing,and it is found that the two UV bands are related to self-trapped holes(STHs)at O1 sites and between two O2-s sites,respectively,and the two blue bands originate from V_(Ga)^(2-)at Ga1 tetrahedral *** photoluminescence mechanism of the films is also *** results will lay a foundation for investigating the Ga_(2)O_(3)film-based electronic devices.

关键词： β-Ga_(2)O_(3) pulsed laser deposition band gap photoluminescence

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Wearable Wellness: Depression Screening via Fitbit data Collected During COVID-19 Pandemic

Wearable Wellness: Depression Screening via Fitbit Data Coll...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Nikola Grozdani America Muñoz Alexander Pietrick Ricardo Flores Avantika Shrestha Xingtong Guo Shichao Liu Elke A. Rundensteiner Computer Science Worcester Polytechnic Institute Worcester MA USA Computer Science/Information Systems National Louis University Chicago IL USA Computer Science/Data Science Worcester Polytechnic Institute Worcester MA USA Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

The COVID-19 pandemic has greatly increased depression among adolescents. The current depression diagnosis process requires significant patient effort and can be costly. Prior research through passively collected data has shown promising depression screening results but is limited by complex data collection and privacy concerns. In this research, we create multiple machine learning models to screen physiological data collected from Fitbit, a wearable biomarker, and depression screening surveys across 166 college students. The highest-scoring model on these physiological modalities achieved an F1-score of 0.92. Our research findings highlight the potential impact of digital technology development in current clinical practices.

关键词： COVID-19 Training Surveys Wearable Health Monitoring Systems Pandemics Biological system modeling Machine learning

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Enhancing Recommendations with Adaptive Multi-Modal Generative Models

Enhancing Recommendations with Adaptive Multi-Modal Generati...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Atharva Digamber Katurde Samiksha Dnyaneshwar Gharge Bhakti Bharat Shinde Soham Vijay Kolapkar Ashwat Anant Nakate Awantika Anil Jadhav Department of Computer Engineering Anantrao Pawar Collage of Engineering and Research Pune India Department of Artificial Intelligence and Data Science Government College Of Engineering Ratnagiri India Department of Computer Engineering P.K. Technical College Of Engineering Pune India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Recommender systems play a critical role in personalizing content, but integrating multiple data modalities and addressing dynamic user preferences presents significant challenges. This paper introduces the Adaptive Multi-Modal Generative Recommender (AMGR) framework, which combines text, image, and video data to enhance recommendation accuracy. By leveraging advanced generative models such as GANs, VAEs, and transformers, AMGR not only suggests content based on user history but also generates personalized content that aligns with evolving preferences. To address the complexities of real-time adaptation, AMGR utilizes reinforcement learning strategies, balancing exploration and exploitation of content. The framework also tackles key technical and ethical challenges, including computational efficiency, multi-modal data fusion, fairness, and privacy. Potential solutions such as federated learning and bias mitigation techniques are explored to ensure responsible and scalable deployment. This paper outlines the proposed methodology, its applications, and the necessary safeguards for AMGR's real-world implementation, contributing to the development of adaptive, fair, and transparent recommendation systems.

关键词： Ethics Adaptation models Adaptive systems Accuracy Scalability data integration Streaming media Transformers Real-time systems Recommender systems

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Public-data assisted private stochastic optimization: power and limitations 24

Public-data assisted private stochastic optimization: power ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora Meta Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science Johns Hopkins University

ISBN: (纸本)9798331314385

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any (ε, δ)-PA-DP has excess risk $\tilde{\Omega}\big(\min(\frac{1}{\sqrt{n_{\text{pub}}}},\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\epsilon} ) \big)$, where d is the dimension, npub is the number of public samples, npriv is the number of private samples, and n = npub + npriv. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with unlabeled public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given Õ(nprivε) unlabeled public samples, achieves the dimension independent rate $\tilde{O}\big(\frac{1}{\sqrt{{n_{\text{priv}}}}} + \frac{1}{\sqrt{{n_{\text{priv}}}\epsilon}}\big)$. We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries.

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Feature fusion-based weighted broad learning system for imbalanced data

Feature fusion-based weighted broad learning system for imba...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Yanting Li Yusha Wang Junwei Jin C. L. Philip Chen College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China College of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Broad Learning System (BLS), characterized by its flat architecture as an efficient neural network, has garnered significant interest for its benefits in terms of training velocity and network scalability. However, BLS does not perform well when dealing with imbalanced classification problems. First, conventional BLS neglects the intrinsic logical information contained in the original input data, which may be critical for classification and prediction. Second, BLS fails to emphasize the significance of minority classes in imbalanced data scenarios. In this paper, we propose a feature fusion-based weighted broad learning system. Unlike traditional BLS, we combine the original features and broad features as fused features for training. Additionally, we introduce a weight generation mechanism that assigns higher weights to minority classes, enhancing the model’s focus on these classes. A closed solution is available for the issue, and the transformation matrix can be effectively resolved. The experimental results on multiple imbalanced datasets show that our method is superior to other imbalanced methods.

关键词： Learning systems Training Mechatronics Adaptive systems Scalability Neural networks Standardization Prediction algorithms Classification algorithms Information technology

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A Novel Privacy-Enhancing Framework for Low-Dose CT Denoising

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Yang, Ziyuan Huangfu, Huijie Ran, Maosong Wang, Zhiwen Yu, Hui Sun, Mengyu Zhang, Yi Sichuan University College of Computer Science Chengdu610065 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China

Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training powerful models with enormous self-collected data and providing application programming interfaces (APIs) for users, such as Chat-GPT. To avoid model leakage, users are required to upload their data to the server. This approach is particularly advantageous for devices with limited computational capabilities, as it offloads computation to the server, easing the workload on the devices themselves. However, this way raises public concerns about the privacy disclosure risk. Hence, to alleviate related concerns, we propose to directly denoise LDCT in the encrypted domain to achieve privacy-preserving cloud services without exposing private data to the server. Concretely, we employ homomorphic encryption to encrypt private LDCT, which is then transferred to the server model trained with plaintext LDCT for further denoising. Since fundamental DL operations, such as convolution and linear transformation, cannot be directly used in the encrypted domain, we transform the fundamental mathematic operations in the plaintext domain into the operations in the encrypted domain. Moreover, we present two interactive frameworks for linear and nonlinear models, both of which can achieve lossless operating. In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage. Moreover, we provide theoretical proof to validate the lossless property of our framework. Finally, experiments were conducted to demonstrate that the transferred contents are well protected and cannot be reconstructed.1 © 2020 IEEE.

关键词： Differential privacy

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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arXiv

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arXiv 2023年

作者： Su, Jian-Nan Gan, Min Chen, Guang-Yong Guo, Wenzhong Philip Chen, C.L. The College of Computer and Data Science Fuzhou University Fuzhou350108 China The School of Computer Science and Engineering South China University of Technology Guangzhou510641 China The Faculty of Science and Technology University of Macau China

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually used the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to the NLA with randomly selected regions stimulated our interest to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends

arXiv

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arXiv 2024年

作者： Liu, Daizong Yang, Mingyu Qu, Xiaoye Zhou, Pan Cheng, Yu Hu, Wei Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Beijing China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

With the significant development of large models in recent years, Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. Compared to traditional Large Language Models (LLMs), LVLMs present great potential and challenges due to its closer proximity to the multi-resource real-world applications and the complexity of multi-modal processing. However, the vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks. Specifically, we first introduce the background of attacks targeting LVLMs, including the attack preliminary, attack challenges, and attack resources. Then, we systematically review the development of LVLM attack methods, such as adversarial attacks that manipulate model outputs, jailbreak attacks that exploit model vulnerabilities for unauthorized actions, prompt injection attacks that engineer the prompt type and pattern, and data poisoning that affects model training. Finally, we discuss promising research directions in the future. We believe that our survey provides insights into the current landscape of LVLM vulnerabilities, inspiring more researchers to explore and mitigate potential safety issues in LVLM developments. The latest papers on LVLM attacks are continuously collected in https://***/liudaizong/Awesome-LVLM-Attack. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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