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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

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Predicting Microsatellite Instability from Histology Images ...

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU 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)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

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arXiv 2023年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Li, Li Gong, Wei Jin, Jian Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Electrical and Computer Engineering Western University ON Canada The Department of Control Science and Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The Research Institute of Industrial Internet of Things CAICT China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services;and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

Validation Methods to Promote Real-world Applicability of Machine Learning in Medicine 3

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Validation Methods to Promote Real-world Applicability of Ma...

3rd International Conference on Digital Medicine and Image Processing, DMIP 2020

作者： Bin Rafiq, Riyad Modave, Francois Guha, Shion Albert, Mark V. Department of Computer Science and Engineering University of North Texas DentonTX United States Department of Health Outcomes and Biomedical Informatics University of Florida United States Department of Computer Science Marquette University MilwaukeeWI United States

ISBN: (纸本)9781450389044

The impact of Artificial Intelligence (AI) on health care has been dramatic;however, there is a considerable degree of skepticism among clinicians about the real-world applicability of advanced predictive models;for this reason, it is particularly important to emphasize the need for proper model validation in machine learning. Often model skepticism is well-placed as modelers may overclaim the real-world replicability for their models, understate the known limitations, or simply not be aware of the hidden limits of the modeling approach. Educational approaches limited to rigorous and thorough justification of all model design decisions may not be practical given model complexity. This also becomes more challenging as state-of-the-art models with the highest benchmark accuracy are becoming less interpretable, e.g. ensemble methods or deep learning. However, in the same way that test-driven development has been a successful paradigm to navigate the complex coding landscape through a focus on testable results, we have observed a similar improvement in modeling strategy when the focus of a predictive model is driven by validation targets rather than more abstract, theoretical concerns. In this study, we provide an overview of the common limitations of model validation methods in medicine. We then present solutions to address such limitations, with a focus on strengthening the validity of predictive models. © 2020 ACM.

关键词： Deep learning

Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas

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arXiv 2023年

作者： Gabay, Yaela Shlezinger, Nir Routtenberg, Tirza Ghasempour, Yasaman Alexandropoulos, George C. Eldar, Yonina C. the School of ECE Ben-Gurion University of the Negev Israel the Department of ECE Princeton University NJ United States the Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60601 United States the Faculty of Math and CS Weizmann Institute of Science Rehovot Israel

Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications. An alternative antenna technology, which can achieve low cost and power efficient THz signaling, is based on leaky wave antennas (LWAs). In this paper, we explore the usage of the LWAs for wideband downlink multiuser THz communications. We propose a model for LWA-aided communication systems that faithfully captures the antenna operations. We that LWAs yield frequency-dependent beams, where the equivalent wideband channel induces dependence between angle, frequency, and spectral lobe width. We identify the LWA’s inherent frequency-selective beamsteering capabilities as motivating multi-band THz communications that deviate from conventional orthogonal frequency-division, and employ nonidentical subbands. Then, we propose an alternating optimization algorithm for jointly optimizing the LWA configuration along with the spectral division and power allocation to maximize the achievable sum-rate. Our numerical results show that a single LWA can generate diverse beampatterns, exhibiting performance comparable to costly fully digital MIMO. Interestingly, we demonstrate that allowing transmission with non-identical subbands leverages the characteristics of LWA-based channels compared to uniform division, yielding improved beamsteering that translate to higher rates Copyright © 2023, The Authors. All rights reserved.

关键词： Antenna lobes

The maximum number of triangles in Fk-free graphs

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arXiv 2022年

作者： Zhu, Xiutao Chen, Yaojun Gerbner, Dániel Győri, Ervin Karim, Hilal Hama Department of Mathematics Nanjing University Nanjing210093 China Alfréd Rényi Institute of Mathematics Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

The generalized Turán number ex(n, Ks, H) is the maximum number of complete graph Ks in an H-free graph on n vertices. Let Fk be the friendship graph consisting of k triangles. Erdős and Sós (1976) determined the value of ex(n, K3, F2). Alon and Shikhelman (2016) proved that ex(n, K3, Fk) ≤ (9k−15)(k+ 1)n. In this paper, by using a method developed by Chung and Frankl in hypergraph theory, we determine the exact value of ex(n, K3, Fk) and the extremal graph for any Fk when n ≥ 4k3 Copyright © 2022, The Authors. All rights reserved.

关键词：

A trustable and interoperable decentralized solution for citizen-centric and cross-border eGovernance: A conceptual approach

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arXiv 2021年

作者： Domalis, George Karacapilidis, Nikos Tsakalidis, Dimitris Giannaros, Anastasios Novelcore Greece IMIS Lab. MEAD University of Patras Greece Computer Engineering and Informatics Department University of Patras Greece

Aiming to support a cross-sector and cross-border eGovernance paradigm for sharing common public services, this paper introduces an AI-enhanced solution that enables beneficiaries to participate in a decentralized network for effective big data exchange and service delivery that promotes the once-only priority and is by design digital, efficient, cost-effective, interoperable and secure. The solution comprises (i) a reliable and efficient decentralized mechanism for data sharing, capable of addressing the complexity of the processes and their high demand of resources;(ii) an ecosystem for delivering mobile services tailored to the needs of stakeholders;(iii) a single sign-on Wallet mechanism to manage transactions with multiple services;and (iv) an intercommunication layer, responsible for the secure exchange of information among existing eGovernment systems with newly developed ones. An indicative application scenario showcases the potential of our approach. © 2021, CC BY.

关键词： Electronic data interchange

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

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TechRxiv

TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

Design of smart system for fruit packinghouse management in supply chain

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Design of smart system for fruit packinghouse management in ...

2020 International Conference on computer Science and Its Application in Agriculture, ICOSICA 2020

作者： Khumaidi, Ali Purwanto, Y. Aris Sukoco, Heru Wijaya, Sony Hartono Darmawan, Risanto University of Krisnadwipayana Department of Informatics Jakarta Indonesia Bogor Agricultural University Department of Mechanical and Biosystem Engineering Bogor Indonesia Bogor Agricultural University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781728169071

Packinghouse management is an integration of a series of business processes and good handling practices. Many previous studies have discussed using technology and methods in each business process at the packinghouse but have not been integrated and are still focused on data input, data processing, and reporting. This research proposes the design of technology and computational integration in a business process called the Smart System for Fruit Packinghouse Management (SPHM) with the aim of accurate fruit classification, product selection, and delivery model to be sent appropriately, selection of appropriate packaging, stock optimization, reduce costs, increase sales and profits, and customer loyalty. This system consists of four sub-systems are Smart Logistic System, Smart Indexing System, Smart Packaging System, and Smart Storage System. Integration of technology and computing proposed are RFID, computer Vision, Vis / NIR, IoT, WSN, AHP, GA, Topsis, RNN, LS-SVM, ABC, HOG, SPA, CARS, SVM, SVR, Fuzzy Logic Control, Time Series Analysis Techniques, and MLFANN. © 2020 IEEE.

关键词： Integration

EXPLOITING PRETRAINED BIOCHEMICAL LANGUAGE MODELS FOR TARGETED DRUG DESIGN

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arXiv 2022年

作者： Uludoğan, Gökçe Ozkirimli, Elif Ulgen, Kutlu O. Karalı, Nilgün Özgür, Arzucan Department of Computer Engineering Bogazici University Istanbul34342 Turkey Data and Analytics Chapter Pharma International Informatics F. Hoffmann-La Roche AG 4303 Switzerland Department of Pharmaceutical Chemistry Istanbul University Istanbul34116 Turkey

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. © 2022, CC BY-NC-SA.

关键词： Molecules