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Improving algorithm for the alignment of consecutive, whole-slide, immunohistochemical section Images

Journal of Pathology Informatics 2021年第1期12卷 29页

作者： Liang, Cher-Wei Chang, Ruey-Feng Fang, Pei-Wei Chen, Chiao-Min Department of Pathology Fu Jen Catholic University Hospital Fu Jen Catholic University New Taipei City Taiwan School of Medicine College of Medicine Fu Jen Catholic University New Taipei City Taiwan Department and Graduate Institute of Pathology National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Most Joint Research Center for Ai Technology and All Vista Healthcare Taipei Taiwan

Background: Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images. Materials and Methods: An automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices. Results: Sets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 μm with a standard deviation of 14.94 μm, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 μm with 50.85 μm standard deviation). Conclusions: Our method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expe

关键词： Cell-level resolution Image registration Immunohistochemistry Multidimensional graph-based registration Proteome topology Three-dimensional tissue reconstruction Whole-slide images

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Generalization Limits of Graph Neural Networks in Identity Effects Learning

arXiv

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arXiv 2023年

作者： D'Inverno, Giuseppe Alessio Brugiapaglia, Simone Ravanelli, Mirco DIISM - University of Siena via Roma 56 Siena53100 Italy Department of Mathematics and Statistics Concordia University 1400 De Maisonneuve Blvd. W. MontréalQCH3G 1M8 Canada Department of Computer Science and Software Engineering Concordia University 2155 Guy St. MontréalQCH3H 2L9 Canada Mila - Quebec AI Institute 6666 Saint-Urbain R. MontréalQCH2S 3H1 Canada

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent in terms of expressive power. In this work, we establish new generalization properties and fundamental limits of GNNs in the context of learning so-called identity effects, i.e., the task of determining whether an object is composed of two identical components or not. Our study is motivated by the need to understand the capabilities of GNNs when performing simple cognitive tasks, with potential applications in computational linguistics and chemistry. We analyze two case studies: (i) two-letters words, for which we show that GNNs trained via stochastic gradient descent are unable to generalize to unseen letters when utilizing orthogonal encodings like one-hot representations;(ii) dicyclic graphs, i.e., graphs composed of two cycles, for which we present positive existence results leveraging the connection between GNNs and the WL test. Our theoretical analysis is supported by an extensive numerical study. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation

arXiv

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arXiv 2024年

作者： Löhr, Thomas Assante, Michele Dodds, Michael Cao, Lili Kabeshov, Mikhail Janet, Jon-Paul Klähn, Marco Engkvist, Ola Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg431 50 Sweden Innovation Centre in Digital Molecular Technologies Department of Chemistry University of Cambridge Lensfield Rd CambridgeCB2 1EW United Kingdom Compound Synthesis & Management The Discovery Centre AstraZeneca 1 Francis Crick Avenue Cambridge Biomedical Campus CambridgeCB2 0AA United Kingdom University of St Andrews St AndrewsKY16 9AJ United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as well as provide parallelization and ease reproducibility. Existing tools represent the computation as a directed acyclic graph (DAG), thus allowing efficient execution by parallelization of concurrent branches. These systems can, however, generally not express cyclic and conditional workflows. We therefore developed Maize, a workflow manager for cyclic and conditional graphs based on the principles of flow-based programming. By running each node of the graph concurrently in separate processes and allowing communication at any time through dedicated inter-node channels, arbitrary graph structures can be executed. We demonstrate the effectiveness of the tool on a dynamic active learning task in computational drug design, involving the use of a small molecule generative model and an associated scoring system, and on a reactivity prediction pipeline using quantum-chemistry and semiempirical approaches. © 2024, CC BY-NC-ND.

关键词： Flow graphs

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Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization

arXiv

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arXiv 2020年

作者： Domingos, Elias Fernández Grujić, Jelena Burguillo, Juan C. Kirchsteiger, Georg Santos, Francisco C. Lenaerts, Tom AI lab Computer Science Department Vrije Universiteit Brussel Pleinlaan 9 3rd floor Brussels1050 Belgium MLG Département d’Informatique Université Libre de Bruxelles Boulevard du Triomphe CP 212 Brussels1050 Belgium Department of Telematic Engineering University of Vigo Vigo36310 Spain ECARES Université Libre de Bruxelles Av. Roosevelt 42 CP 114 Brussels1050 Belgium INESC-ID Instituto Superior Técnico Universidade de Lisboa IST-Taguspark Porto Salvo2744-016 Portugal ATP-group Porto Salvo2744-016 Portugal

Human social dilemmas are often shaped by actions involving uncertain goals and returns that may only be achieved in the future. Climate action, voluntary vaccination and other prospective choices stand as paramount examples of this setting 1–4. In this context, as well as in many other social dilemmas, uncertainty may produce non-trivial effects 5. Whereas uncertainty about collective targets and their impact were shown to negatively affect group coordination and success 6–8, no information is available about timing uncertainty, i.e. how uncertainty about when the target needs to be reached affects the outcome as well as the decision-making. Here we show experimentally — through a collective dilemma wherein groups of participants need to avoid a tipping point under the risk of collective loss8 — that timing uncertainty prompts not only early generosity9 but also polarized contributions, in which participants’ total contributions are distributed more unfairly than when there is no uncertainty. Analyzing participant behavior reveals, under uncertainty, an increase in reciprocal strategies wherein contributions are conditional on the previous donations of the other participants, a group analogue of the well-known Tit-for-Tat10,11 strategy. Although large timing uncertainty appears to reduce collective success, groups that successfully collect the required amount show strong reciprocal coordination. This conclusion is supported by a game theoretic model examining the dominance of behaviors in case of timing uncertainty. In general, timing uncertainty casts a shadow on the future11 that leads participants to respond early, encouraging reciprocal behaviors, and unequal *** Codes 91 Copyright © 2020, The Authors. All rights reserved.

关键词： Timing circuits

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Single electrons on solid neon as a solid-state qubit platform

arXiv

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arXiv 2021年

作者： Zhou, Xianjing Koolstra, Gerwin Zhang, Xufeng Yang, Ge Han, Xu Dizdar, Brennan Li, Xinhao Divan, Ralu Guo, Wei Murch, Kater W. Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States James Franck Institute Department of Physics University of Chicago ChicagoIL60637 United States National High Magnetic Field Laboratory TallahasseeFL32310 United States Department of Mechanical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL32310 United States Department of Physics Washington University in St. Louis St. LouisMO63130 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States

Progress toward the realization of quantum computers requires persistent advances in their constituent building blocks - qubits. Novel qubit platforms that simultaneously embody long coherence, fast operation, and large scalability offer compelling advantages in the construction of quantum computers and many other quantum information systems 1-3. Electrons, ubiquitous elementary particles of nonzero charge, spin, and mass, have commonly been perceived as paradigmatic local quantum information carriers. Despite superior controllability and configurability, their practical performance as qubits via either motional or spin states depends critically on their material environment 3-5. Here we report our experimental realization of a new qubit platform based upon isolated single electrons trapped on an ultraclean solid neon surface in vacuum 6-13. By integrating an electron trap in a circuit quantum electrodynamics architecture 14-20, we achieve strong coupling between the motional states of a single electron and a single microwave photon in an on-chip superconducting resonator. Qubit gate operations and dispersive readout are implemented to measure the energy relaxation time T1 of 15 µs and phase coherence time T2 over 200 ns. These results indicate that the electron-on-solid-neon qubit already performs near the state of the art as a charge qubit 21 Copyright © 2021, The Authors. All rights reserved.

关键词： Electrons

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Evaluation of Openai o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of Openai's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

arXiv

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arXiv 2022年

作者： Nguyen, Tien-Phat Pham, Trong-Thang Nguyen, Tri Le, Hieu Nguyen, Dung Lam, Hau Nguyen, Phong Fowler, Jennifer Tran, Minh-Triet Le, Ngan FPT Software AI Center Ho Chi Minh City Viet Nam University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam John von Neumann Institute Vietnam National University Ho Chi Minh City Viet Nam IVFMD My Duc Phu Nhuan hospital Ho Chi Minh City Viet Nam Olea Fertility Vinmec Central Park International Hospital Ho Chi Minh City Viet Nam HOPE Research Center My Duc Hospital Ho Chi Minh City Viet Nam Arkansas Economic Development Commission Little RockAR72202 United States Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR72703 United States

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head. Both contracting and expanding paths are synergetically learned by a collaboration head. We have benchmarked our proposed EmbryosFormer on two datasets: a public dataset with mouse embryos with 8-cell stage and an in-house dataset with human embryos with 4-cell stage. Copyright © 2022, The Authors. All rights reserved.

关键词： Deformation

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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

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Sensor integrated modules – Social distance & hygiene solutions for COVID-19 pandemic

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aiP Conference Proceedings 2024年第1期2512卷

作者： Kalyan Dusarlapudi Narasimha Raju Kuthuri Kattupalli Sudhakar Prathyusha Chennupati Shiny Syam Mallolu Sai Harshitha Padala Venkata Siva Kumari Narayanam Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Senior Assistant Professor Department of AI&DS Lakireddy Balireddy College of Engineering Mylavaram India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Communication and soft skills department Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India

The Covid-19 pandemic has opened doors to the academic community to explore more possibilities for innovations. The present paper discusses three prototypes developed for the user’s safety during the pandemic lifestyle. The modules offer alert and hygiene practices to helps users not to get affected by the Covid-19 Virus. The first module intended for user alerts to maintain social distance concerning nearest humans. The module prompts the buzzer if a person comes near within one-meter vicinity. The Second module is an automatic hand sanitiser dispenser for self-clean hands with disinfectant liquid. It uses IR sensors for hand proximity detection to trigger a pump to deliver sanitiser to the hands. The last module dispenses water into a glass. The sensors incorporated are ultrasound and IR sensors for glass detection, automatic water level and user water level control. These modules components, methodology and operation, are discussed. The cost-effective modules offer a long-standing performance with an Embedded board.

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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

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Healthcare Analytics 2024年 6卷 100362-100362页

作者： Ahmed, Rasel Fahad, Nafiz Miah, Md Saef Ullah Hossen, Md. Jakir Morol, Md. Kishor Mahmud, Mufti Mostafizur Rahman, M. Faculty of Science and Technology American International University-Bangladesh Kuratoli Khilkhet Dhaka 1229 Bangladesh Faculty of Information Science and Technology (FIST) Multimedia University Melaka Malaysia Faculty of Engineering and Technology (FET) Multimedia University Jalan Ayer Keroh Lama 75450 Bukit Beruang Melaka Malaysia Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Nottingham NG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Nottingham NG11 8NS United Kingdom Department of Mathematics American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computing and Information Science Cornell University NY United States EliteLab.Ai NY United States DataTech Research and Innovation Learning Lab Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model's effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model's predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation. © 2024 The Authors

关键词： Dementia prediction Early detection Logistic regression Recursive feature elimination Shapley Additive exPlanations (SHAP)

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