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Survey on deep learning in multimodal medical imaging for cancer detection

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arXiv 2023年

作者： Tian, Yan Xu, Zhaocheng Ma, Yujun Ding, Weiping Wang, Ruili Gao, Zhihong Cheng, Guohua He, Linyang Zhao, Xuran School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China School of Mathematical and Computational Sciences Massey University Auckland0632 New Zealand School of Information Science and Technology Nantong University Nantong226019 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325000 China Jianpei Technology Co. Ltd Hangzhou311200 China

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer vision

Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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Probabilistic partition of unity networks for high-dimensional regression problems

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arXiv 2022年

作者： Fan, Tiffany Trask, Nathaniel D'Elia, Marta Darve, Eric Institute for Computational and Mathematical Engineering Stanford University CA United States Center for Computing Research Sandia National Laboratories New Mexico United States Data Science and Computing Sandia National Laboratories CA United States Department of Mechanical Engineering Stanford University CA United States

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a local fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients;and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly parallelizable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. We also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum computers

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

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Towards a Quantum Algorithm for the Incompressible Nonlinear...

Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Muralikrishnan Gopalakrishnan Meena Yu Zhang Weiwen Jiang Youzuo Lin Stefanie Günther Xinfeng Gao National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Theoretical Division Los Alamos National Laboratory Los Alamos NM USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville VA USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

关键词： Computers Quantum algorithm Tensors Fluids Partial differential equations Encoding Transient analysis Nonlinear systems Physics Navier-Stokes equations

Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations Research Forum 2023年第1期4卷 16页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

Almost affinely disjoint subspaces

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arXiv 2020年

作者： Liu, Hedongliang Polianskii, Nikita Vorobyev, Ilya Wachter-Zeh, Antonia Technical University of Munich Institute for Communications Engineering Munich80333 Germany Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Moscow121205 Russia

In this work, we introduce a natural notion concerning finite vector spaces. A family of k-dimensional subspaces of Fnq, which forms a partial spread, is called almost affinely disjoint if any (k + 1)-dimensional subspace containing a subspace from the family non-trivially intersects with only a few subspaces from the family. The central question discussed in the paper is the polynomial growth (in q) of the maximal cardinality of these families given the parameters k and n. For the cases k = 1 and k = 2, optimal families are constructed. For other settings, we find lower and upper bounds on the polynomial growth. Additionally, some connections with problems in coding theory are shown. © 2020, CC BY-NC-ND.

关键词： Vectors

Measurement of the Pu239(n,f) cross section from 4 keV to 100 MeV using the white neutron source at the CSNS Back-n facility

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Physical Review C 2023年第2期107卷 024606-024606页

作者： Yijia Qiu Changlin Lan Yonghao Chen Liyang Jiang Jie Bao Yiwei Yang Zhongwei Wen Rong Liu Xichao Ruan Jingyu Tang Jie Ren Hantao Jing Guangyuan Luan Ruirui Fan Yangbo Nie Xianlin Yang Xiaojun Li Han Yi Wei Jiang Tao Ye Yi Yang Shilong Liu Jincheng Wang School of Nuclear Science and Technology Lanzhou University Lanzhou 730000 China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang 621900 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

The neutron-induced fission cross section of Pu239 was measured relative to U235(n,f) at the back-streaming white neutron beam line (Back-n) of the China Spallation Neutron Source (CSNS). A multicell fast fission ionization chamber was used to perform the measurement. The reliability of the measurement was verified by the high consistency of U235's resonances between the measurement and the evaluation data. The Pu239 fission cross sections from 4keV to 100MeV are obtained with 1.7–5.8 % uncertainty when unfolding uncertainties are excluded. The total uncertainties including the unfolding errors, which reflect the effect of the double-bunch unfolding method, are 2.6–15 % from 10keV to 100MeV.

关键词： Fission A ≥ 220 Spectrometers & spectroscopic techniques

On inf-convolution-based robust practical stabilization under computational uncertainty

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arXiv 2021年

作者： Schmidt, Patrick Osinenko, Pavel Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Lab Chemnitz09126 Germany Computational and Data Science and Engineering Center Skolkovo Institute of Science and Technology Moscow143026 Russia

This work is concerned with practical stabilization of nonlinear systems by means of inf-convolution-based sample-and-hold control. It is a fairly general stabilization technique based on a generic non-smooth control Lyapunov function (CLF) and robust to actuator uncertainty, measurement noise, etc. The stabilization technique itself involves computation of descent directions of the CLF. It turns out that non-exact realization of this computation leads not just to a quantitative, but also qualitative obstruction in the sense that the result of the computation might fail to be a descent direction altogether and there is also no straightforward way to relate it to a descent direction. Disturbance, primarily measurement noise, complicate the described issue even more. This work suggests a modified inf-convolution-based control that is robust w. r. t. system and measurement noise, as well as computational uncertainty. The assumptions on the CLF are mild, as, e. g., any piece-wise smooth function, which often results from a numerical LF/CLF construction, satisfies them. A computational study with a three-wheel robot with dynamical steering and throttle under various tolerances w. r. t. computational uncertainty demonstrates the relevance of the addressed issue and the necessity of modifying the used stabilization technique. Similar analyses may be extended to other methods which involve optimization, such as Dini aiming or steepest descent. © 2021, CC BY-NC-ND.

关键词： Nonlinear systems