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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

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Heliyon 2024年第21期10卷 e39279页

作者： Qureshi, Shahnawaz Iqbal, Syed Muhammad Zeeshan Ameer, Asif Karrila, Seppo Ghadi, Yazeed Yasin Shah, Syed Aziz Intelligent Biomedical Application Lab Sino-Pak center for Artificial Intelligence School of Computing Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur 22620 Pakistan Research and Development BrightWare LLC Riyadh 13323 Saudi Arabia Department of Computer Science National University of Computing and Emerging Sciences Faisalabad 38000 Pakistan Faculty of Science and Industrial Technology Prince of Songkla University Surat Thani Campus Muang Surat Thani 84000 Thailand Department of Computer Science Al Ain University Abu Dhab Al Ain United Arab Emirates Healthcare Sensing Technology Faculty Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom

Background and Objective: Emerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. Methods: In this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization Results: High recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently h

关键词： Convolutional neural network Drug target interaction Intracranial EEG Local field potential Long short-term memory

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study 7

Automated Detection of Brain Abnormality using Deep-Learning...

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7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

作者： Kadry, Seifedine Nam, Yunyoung Rauf, Hafiz Tayyab Rajinikanth, Venkatesan Lawal, Isah A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway Soonchunhyang University Department of Computer Science and Engineering Asan-si Korea Republic of Staffordshire University Centre for Smart Systems Ai and Cybersecurity Stoke-on-Trent United Kingdom St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India

ISBN: (纸本)9781665441261

Brain is the vital organ in human physiology;which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes;normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Translating HCI Research to Broader Audiences: Motivation, Inspiration, and Critical Factors on Alternative Research Outcomes 25

Translating HCI Research to Broader Audiences: Motivation, I...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Yoo, MinYoung Ppali, Sophia Odom, William Zhuang, Yumeng Kritika, Kritika Olson, Wyatt Wieczorek, Catherine Biggs, Heidi Berger, Arne Desjardins, Audrey Wakkary, Ron Ringland, Kathryn E. School of Interactive Arts and Technology Simon Fraser University Surrey BC Canada Hci Research Group Cyens Centre of Excellence Nicosia Cyprus Computational Media Department University of California Santa Cruz Santa Cruz United States School of Art + Art History + Design University of Washington Seattle WA United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States School of Literature Media and Communication Georgia Institute of Technology Atlanta GA United States Computer Science and Languages Anhalt University of Applied Sciences Koethen Germany

ISBN: (纸本)9798400713941

Alternative Research Outcomes (AROs) go beyond traditional academic publications, taking diverse forms such as documentaries, DIY tutorials, or exhibitions. With growing recognition of the need for more inclusive and contextually appropriate research dissemination, AROs are particularly relevant in HCI and design research. Yet, little has been discussed on why it is important to work on AROs. What are key qualities of AROs? How can the HCI community benefit from learning more about creating AROs? By analyzing six case studies, we propose four qualities of AROs and demonstrate how they emerge in the timeline of a research project. We argue AROs can be adapted to diverse audience needs and share research insights that may extend beyond the original research goals. Our work contributes to a deeper understanding of how AROs can support inclusive research dissemination practices, enabling HCI researchers to engage broader audiences and extend the relevance of their work. © 2025 Copyright held by the owner/author(s).

关键词： Alternative Forms Art Installation Audio/Video Documentary Digital Media Content DIY Tutorial Knowledge Production Research Communication Research Outcomes Zine

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Hierarchical quantum embedding by machine learning for large molecular assemblies

arXiv

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arXiv 2025年

作者： Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus ETH Zurich Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 2 Zurich8093 Switzerland University of Copenhagen Department of Chemistry and Nano-Science Center Universitetsparken 5 Copenhagen ØDK-2100 Denmark University of Copenhagen Niels Bohr Institute NNF Quantum Computing Programme Blegdamsvej 17 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Computer Science Universitetsparken 1 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Biology Linderstrøm-Lang Centre for Protein Science Ole Maaløes Vej 5 Copenhagen NDK-2200 Denmark University of Copenhagen Center for Health Data Science Department of Public Health Øster Farimagsgade 5 Copenhagen ØDK-1353 Denmark

We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and non-equilibrium switching simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecules

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Towards Understanding the Transmission of Listeriosis in Pregnant Women and Newborns: A Mathematical Modeling Study

SSRN

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SSRN 2024年

作者： Chukwu, C.W. Tchoumi, Stephane Yanick Mushanyu, J. Chazuka, Z. Madubueze, C.E. Department of Mathematics Wake Forest University Winston-SalemNC27109 United States Department of Mathematics Joseph Sarwuan Tarka University Makurdi Nigeria Department of Mathematics and Computer Sciences ENSAI University of NGaoundere P.O. Box 455 Ngaoundere Cameroon Department of Mathematics and Applied Mathematics University of Pretoria Pretoria South Africa Department of Computing Mathematical & Statistical Science University of Namibia Windhoek13301 Namibia Department of Decision Sciences College of Economics and Management Sciences University of South Africa South Africa

Listeriosis, a foodborne infection from consuming Listeria bacteria within ready-to-eat (RTE) products,remains a critical global health concern, particularly for high-risk individuals such as pregnant women,the elderly, and newborns. This paper presents a non-linear deterministic model of Listeriosis thatincorporates pregnant women, newborns, and food products. The model undergoes analysis to establishsteady states and their stabilities, and the reproduction number is computed to examine the transmissiondynamics of the disease. Sensitivity analysis, using the Latin Hypercube Sampling (LHS) technique, isconducted, revealing the significant impact of rates of manufacturing uncontaminated food, contaminationof food products, and food product removal on disease spread. Additionally, the recovery of infectedpregnant women and their susceptibility rates directly affect the prevalence of Listeriosis. Simulationresults reveal the importance of increasing the recovery rate of infected pregnant women as the mosteffective strategy for reducing Listeriosis among pregnant women and newborns. Achieving this goalnecessitates enhancing treatment/medical interventions and increasing the rate of food product removalto combat the disease effectively © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Physics-Regulated Digital Backpropagation for Optical Fiber Systems with Imprecise Parameters

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IEEE Transactions on Communications 2024年

作者： Zhang, Jiahao Zhang, Lu Yu, Xianbin Wang, Zhanhong Ozolins, Oskars Pang, Xiaodan Brandt-Pearce, Maite Zhang, Qun Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Shandong Zhike Intelligence Computing Co. Ltd Jinan250101 China KTH Royal Institute of Technology Applied Physics Department Stockholm106 91 Sweden RISE Research Institutes of Sweden Networks Unit Kista164 40 Sweden Riga Technical University Institute of Telecommunications Riga1048 Latvia University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

Current signal processing algorithms excel at impairment compensation when the parameters of optical fiber systems are precisely defined. However, their effectiveness diminishes considerably in the presence of imprecise parameters. By integrating 'knowledge application from physics to neural networks (NN)' with 'information feedback from NNs to physics', this paper proposes a physics-regulated digital backpropagation (PR-DBP) algorithm, which shows great promise for impairment compensation with imprecise fiber parameters. The PR-DBP employs an optimization-estimation-initialization loop structure. The optimization process provides the network's adaptability by minimizing the loss value as in conventional NNs. The estimation process dynamically tracks physical parameters by extracting information from NNs to the physical domain. The initialization process offers a physics-based global control over the neural network, thereby mitigating the risk of overfitting by preventing an excessive focus on loss minimization. Moreover, a phase-shift weight function is applied to further improve algorithmic efficiency. Numerical analyses indicate that the PR-DBP significantly outperforms conventional methods in optical fiber systems with imprecise parameters, achieving a bit error rate reduction by an order of magnitude. As a mutually reinforcing part of impairment compensation, a high-accuracy and adaptive fiber parameter estimation is also demonstrated. © 1972-2012 IEEE.

关键词： Structural optimization

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Models of Battle Dynamics in Strategic computer Games

Models of Battle Dynamics in Strategic Computer Games

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Ivan Dmitrievich Laryushin Dmitrii Sergeevich Pashin Yaroslav Alekseevich Koltochenko Felix Ivanovich Ereshko Plasma Physics Department Gaponov-Grekhov Institute of Applied Physics RAS Nizhny Novgorod Russia Faculty of Physics National Research Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Mathematical Modeling of Complex Systems and Optimization Moscow Institute of Physics and Technology Moscow Russia Department of Information and Computing Systems Federal Research Center "Computer Science and Control" of the RAS Moscow Russia

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game.

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Quantum meets the minimum circuit size problem

arXiv

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arXiv 2021年

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States School of Engineering and Applied Sciences Harvard University Department of Computer Science Boston University Department of Computer Science University of Texas Austin United States

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory — its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © 2021, CC BY.

关键词： Boolean functions

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