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arXiv 2024年

作者： Durrer, Alicia Wolleb, Julia Bieder, Florentin Friedrich, Paul Melie-Garcia, Lester Ocampo-Pineda, Mario Bercea, Cosmin I. Hamamci, Ibrahim E. Wiestler, Benedikt Piraud, Marie Yaldizli, Özgür Granziera, Cristina Menze, Bjoern H. Cattin, Philippe C. Kofler, Florian Department of Biomedical Engineering University of Basel Switzerland University Hospital Basel Switzerland Computational Imaging and AI in Medicine Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Germany Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Germany TranslaTUM Central Institute for Translational Cancer Research Technical University of Munich Germany

Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for the evaluation of volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy tissue. To enable the evaluation of scans containing pathological tissue, it is therefore required to restore healthy tissue in the pathological areas. In this work, we explore and extend denoising diffusion models for consistent inpainting of healthy 3D brain tissue. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error. To emphasize the clinical relevance, we fine-tune this model on data containing synthetic MS lesions and evaluate it on a downstream brain tissue segmentation task, whereby it outperforms the established FMRIB Software Library (FSL) lesion-filling method. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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CT Scan Synthesis for Promoting computer-Aided Diagnosis Capacity of COVID-19 16th

CT Scan Synthesis for Promoting Computer-Aided Diagnosis Cap...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Attention-based multi-level feature fusion for named entity recognition 29

Attention-based multi-level feature fusion for named entity ...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

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Possible Contribution to Data-Driven Primate Research: Comment on “Kinematic coding: Measuring Information in Naturalistic Behaviour” by Becchio, Pullar, Scaliti, and Panzeri

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Physics of Life Reviews 2025年

作者： Koki Mimura Ken Nakae Kenji Doya Department of Mental Disorder Research National Institute of Neuroscience National Center of Neurology and Psychiatry Tokyo 187-8502 Japan Graduate School of Engineering University of Fukui Fukui 910-8507 Japan Neural Computation Unit Okinawa Institute of Science and Technology Graduate University Okinawa 904-0495 Japan

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Quantum nutcracker for near-room-temperature H_2 dissociation

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science Bulletin 2019年第1期64卷 4-7页

作者： Lei Tao Wei Guo Yu-Yang Zhang Yan-Fang Zhang Jiatao Sun Shixuan Du Sokrates T.Pantelides Institute of Physics & University of Chinese Academy of Sciences Chinese Academy of Sciences School of Physics Beijing Institute of Technology CAS Center for Excellence in Topological Quantum Computation Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt University

Solid surfaces are well known to mediate the dissociation of molecules via electronic interactions (heterogeneous catalysis).In particular,H2 dissociation on metal surfaces has been widely studied for several decades because it is an important step in hydrogenation *** efficiency of the process depends on both the electronic properties of the metal surface and the surface microstructures [1].Enhanced efficiency and reduced cost are usually achieved by using nanoparticles,which have increased the surface-to-volume ratio and low-coordination atoms [2].Another approach is to optimize the local electronic states of the metal surface by doping [3],alloying with other elements [4],or by taking advantage of strong interactions between metal nanoparticles and the supporting substrate [5].These methods often work together to tailor the adsorption properties on surfaces and show major efficiency enhancement.

关键词： Quantum nutcracker Electron interaction The electronic properties

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Nonlinear feedforward enabling quantum computation

arXiv

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arXiv 2022年

作者： Sakaguchi, Atsushi Konno, Shunya Hanamura, Fumiya Asavanant, Warit Takase, Kan Ogawa, Hisashi Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Huntington, Elanor Yonezawa, Hidehiro Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology and Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

Measurement-based quantum computation with optical time-domain multiplexing is a promising method to realize a quantum computer from the viewpoint of scalability. Fault tolerance and universality are also realizable by preparing appropriate resource quantum states and electro-optical feedforward that is altered based on measurement results. While a linear feedforward has been realized and become a common experimental technique, nonlinear feedforward was unrealized until now. In this paper, we demonstrate that a fast and flexible nonlinear feedforward realizes the essential measurement required for fault-tolerant and universal quantum computation. Using non-Gaussian ancillary states we observed 10% reduction of the measurement excess noise relative to classical vacuum ancilla. © 2022, CC BY.

关键词： Fault tolerance

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PUBLISHING neural NETWORKS IN DRUG DISCOVERY MIGHT COMPROMISE TRAINING DATA PRIVACY

arXiv

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arXiv 2024年

作者： Krüger, Fabian P. Östman, Johan Mervin, Lewis Tetko, Igor V. Engkvist, Ola AstraZeneca R&D Discovery Sciences Molecular AI Mölndal431 83 Sweden Technical University of Munich TUM School of Computation Information and Technology Department of Mathematics Munich80333 Germany Helmholtz Munich Molecular Targets and Therapeutics Center Institute of Structural Biology Neuherberg85764 Germany AI Sweden Gothenburg41756 Sweden AstraZeneca R&D Discovery Sciences Molecular AI CambridgeCB2 0AA United Kingdom Chalmers University of Technology Department of Computer Science and Engineering Gothenburg412 96 Sweden

This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations. Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Behavior and properties of water in silicate melts under deep mantle conditions

Research Square

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Research Square 2021年

作者： Karki, Bijaya B. Ghosh, Dipta B. Karato, Shun-Ichiro School of Electrical Engineering and Computer Science Department of Geology and Geophysics Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Dpeartment of Earth and Planetary Sciences Yale University New HavenCT06520 United States

Water (H2O) as one of the most abundant fluids present in Earth plays crucial role in the generation and transport of magmas in the interior. Though hydrous silicate melts have been studied extensively, the experimental data are confined to relatively low pressures and the computational results are still rare. Moreover, these studies imply large differences in the way water influences the physical properties of silicate magmas, such as density and electrical conductivity. Here, we invesigate the equation of state, speciation, and transport properties of water dissolved in Mg1-xFexSiO3 and Mg2(1-x)Fe2xSiO4 melts (for x = 0 and 0.25) as well as in its bulk (pure) fluid state over the entire mantle pressure regime at 2000 to 4000 K using first-principles molecular dynamics. The simulation results allow us to constrain the partial molar volume of the water component in melts along with the molar volume of pure water. The predicted volume of silicate melt+water solution is negative at low pressures but becomes zero above 15 GPa implying ideal mixing at higher pressures. Consequently, the hydrous component tends to lower the melt density to similar extent over much of the mantle pressure regime irrespective of composition. Our results also show that hydrogen diffuses fast in silicate melts and enhances the melt electrical conductivity in a way that differs from electrical conduction in the bulk water. The speciation of the water component varies considerably from the bulk water structure as well. Water is dissolved in melts mostly as hydroxyls at low pressure and as -O-H-O-, -O-H-O-H- and other extended species with increasing pressure. On the other hand, the pure water behaves as a molecular fluid below 15 GPa, gradually becoming a dissociated fluid with further compression. On the basis of modeled density and conductivity results, we suggest that partial melts containing a few percent of water may be gravitationally trapped both above and below the upper mantle-transiti

关键词： Iron compounds

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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