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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific Reports 2025年第1期15卷 1-23页

作者： Adnan, Khan Muhammad Ghazal, Taher M. Saleem, Muhammad Farooq, Muhammad Sajid Yeun, Chan Yeob Ahmad, Munir Lee, Sang-Woong Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 South Korea Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates University College Korea University Seoul 02841 South Korea

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model’s predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered “black box” systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery

arXiv

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arXiv 2022年

作者： Ghorbanzadeh, Omid Xu, Yonghao Zhao, Hengwei Wang, Junjue Zhong, Yanfei Zhao, Dong Zang, Qi Wang, Shuang Zhang, Fahong Shi, Yilei Zhu, Xiao Xiang Bai, Lin Li, Weile Peng, Weihang Ghamisi, Pedram Landstraβer Hauptstraβe 5 Vienna1030 Austria The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University 430074 China The School of Artificial Intelligence Xidian University Xian710071 China The Data Science in Earth Observation Technical University of Munich Munich80333 Germany Remote Sensing Technology Technical University of Munich Munich80333 Germany Wessling82234 Germany Munich80333 Germany The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology No.1 East Third Road Erxianqiao Chenghua District Chengdu610059 China Landstraβer Hauptstraβe 5 Vienna1030 Austria Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Machine Learning Group Chemnitzer Str. 40 Freiberg09599 Germany

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in artificial intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretation, due to the development of convolutional neural networks (CNNs). The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models like the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies such as hard example mining, self-training, and mix-up data augmentation are also considered. Moreover, we describe the L4S benchmark data set in order to facilitate further comparisons, and report the results of the accuracy assessment online. The data is accessible on Future Development Leaderboard for future evaluation at https://www. ***/landslide4sense/challenge/, and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article. Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models

Results in Engineering

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Results in engineering 2025年 27卷

作者： Farhan Amir Fardush Sham Ahmed El-Shafie Wan Zurina Wan Jaafar Adarsh S Mohsen Sherif Ali Najah Ahmed Department of Civil Engineering Faculty of Engineering Universiti Malaya (UM) 50603 Kuala Lumpur Malaysia National Water and Energy Center United Arab Emirates University Al Ain P.O.Box 15551 United Arab Emirates TKM College of Engineering Kollam 691005 India APJ Abdul Kalam Technological University Thiruvananthapuram Kerala 695016 India Civil and Environmental Engineering Department College of Engineering United Arab Emirates University 15551 Al Ain United Arab Emirates Research Centre For Human-Machine Collaboration (HUMAC) School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway 47500 Selangor Darul Ehsan Malaysia School of Engineering Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway 47500 Selangor Darul Ehsan Malaysia

Accurate rainfall forecasting is crucial for managing water resources, supporting agriculture, and preparing for natural disasters, especially as climate variability becomes more pronounced. Traditional methods often struggle with the complexity and unpredictability of rainfall patterns across different climates, driving interest in advanced modelling approaches capable of capturing such nonlinear dynamics. artificial Intelligent (AI) model are well-suited for detecting complex temporal pattern in rainfall data, enabling improved short-, medium- and long-term performance. A key feature of the purposed framework is the integration of AI models with climate change simulation output through a fusion process that combines historical climate projections and rainfall data using a hybrid input strategy. This fusion enables the AI models to learn not only from observed rainfall sequences but also from anticipated climate-driven variations, thus improving the model’s adaptability and robustness under changing environmental conditions. These findings highlight the potential of more reliable and resilient forecasting systems that support informed decision-making in agriculture, urban planning, and disaster preparedness, reinforcing the promise of AI in climate-aware rainfall prediction.

关键词： Rainfall forecasting Forecasting model methods Ai modeling Climate Change

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Self-weighted robust LDA for multiclass classification with edge classes

arXiv

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arXiv 2020年

作者： Yan, Caixia Luo, Minnan Chang, Xiaojun Zheng, Qinghua Zhang, Xiaoqin Li, Zhihui Nie, Feiping School of Electronic and Information Engineering Xi’an Jiaotong University China Faculty of Information Technology Monash University Australia College of Computer Science and Artificial Intelligence Wenzhou University China Qilu University of Technology Shandong Academy of Sciences China Center for Optical Image Analysis and Learning Northwestern Polytechnical University China

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of `2-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with `2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging `2,1-norm maximization problem. The proposed SWRLDA is easy to implement, and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets, while presenting superior computational efficiency in comparison with other techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational efficiency

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Retraction Note: Association Strategies of Speech Behavior of Communicators in Coding Discourse: An Interdisciplinary Approach Towards Understanding the Role of Cognitive and Linguistic Processes in Communication

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Journal of psycholinguistic research 2025年第2期54卷 20页

作者： Ludan Zhang Irina Karabulatova Artem Nurmukhametov Margarita Lagutkina Department of Foreign Languages of Philological Faculty Peoples Friendship University of Russia Named after Patrice Lumumba RUDN University) Moscow Russia. Department of Foreign Languages of Philological Faculty Peoples Friendship University of Russia Named after Patrice Lumumba RUDN University) Moscow Russia. radogost2000@mail.ru. Laboratory of Machine Learning and Semantic Analysis Institute of Artificial Intelligence Lomonosov Moscow State University Moscow Russia. radogost2000@mail.ru. Department of Machine Learning and Digital Humanities Moscow Institute of Physics and Technology (National University) Dolgoprudny Russia. radogost2000@mail.ru. Department of Information Processing and Control Systems Bauman Moscow State Technological University Moscow Russia. radogost2000@mail.ru. Institute of Humanities Yugra State University Khanty-Mansiysk Russia.

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AI-ML-Powered IoT approach for designing a smart wheelchair for individuals with disabilities

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AIP Conference Proceedings 2024年第1期3149卷

作者： J. Kamalakumari A. Archudha R. Kiruthikaa N. Saranya S. Kaliappan G. Pavithra T. C. Manjunath Department of BCA SRMIST Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Shavigemalleshwara hills Kumarasamy Layout Bengaluru Karnataka India Department of ECE Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Division of Research and Development Lovely Professional University Jalandhar - Delhi G.T. Road Phagwara Punjab India Dept. of Electronics & Communication Engg. Dayananda Sagar College of Engg. Bengaluru Karnataka India

Intelligent Wheelchairs represent mechanical devices designed to empower individuals with the ability to move independently. This results in a reduction of the user’s energy expenditure and effort required to maneuver the wheels. Additionally, these devices facilitate the movement of visually or physically challenged individuals between different locations. The wheelchair is equipped with a system that recognizes obstacles, thereby minimizing the risk of accidents during transportation. Recently, there has been a growing fascination with smart wheelchairs, which prove particularly advantageous for seamless transportation. These devices find application not only’s for personal use but also in nursing homes, aiding elderly individuals facing mobility challenges. They are particularly in-valuable for those who has lost their mobility. While various type of intelligently designed wheel-chairs has been developing in the past, ongoing advancements introduce new generations with artificial intelligence, offering users a ranges of options. People with life-long disability resulted from accidents, paralysis, or aging often depend on others for assistance. The incorporation of intelligent healthcare systems, including remote health monitoring, enhances their independence by allowing continuous health tracking without requiring active effort from the individual. This is especially beneficial for those facing difficulties accessing traditional medical systems. In today’s world, numerous individuals with disabilities encounter obstacles in their daily lives and mobility. While many rely on external support, the integration of assistive technologies enables them for achieving the degree of self-independence. Among these technologies, wheelchairs are the most commonly utilized. However, some individuals with weakened joints and limbs struggle to maneuver traditional wheelchairs, highlighting the potential benefits of smart wheelchairs. Electric-powered smart wheelchairs comes with

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DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology

arXiv

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arXiv 2024年

作者： Attafi, Omar Abdelghani Clementel, Damiano Kyritsis, Konstantinos Capriotti, Emidio Farrell, Gavin Fragkouli, Styliani-Christina Castro, Leyla Jael Hatos, András Lenaerts, Tom Mazurenko, Stanislav Mozaffari, Soroush Pradelli, Franco Ruch, Patrick Savojardo, Castrense Turina, Paola Zambelli, Federico Piovesan, Damiano Monzon, Alexander Miguel Psomopoulos, Fotis Tosatto, Silvio C.E. Department of Biomedical Sciences University of Padova Italy Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Department of Pharmacy and Biotechnology University of Bologna Bologna Italy ELIXIR Hub Hinxton Cambridge United Kingdom Department of Biology National and Kapodistrian University of Athens Athens Greece ZB Med Information Centre for Life Sciences Cologne Germany Department of Oncology Geneva University Hospitals Geneva Switzerland Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Swiss Cancer Center Léman Lausanne Switzerland Interuniversity Institute of Bioinformatics in Brussels Université Libre de Bruxelles Vrije Universiteit Brussel Brussels1050 Belgium Machine Learning Group Université Libre de Bruxelles Street Belgium Artificial Intelligence Laboratory Vrije Universiteit Brussels Brussels Belgium Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Czech Republic Masaryk University Brno Czech Republic International Clinical Research Centre St. Anne's Hospital Brno Czech Republic HES-SO - HEG Geneva Geneva Switzerland SIB Swiss Institute of Bioinformatics Geneva Switzerland Dept. of Biosciences University of Milan Italy Bari Italy Department of Information Engineering University of Padova Italy Bari Italy

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry, a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences. The DOME Registry can be freely accessed from URL: ***. © 2024, CC BY-NC-ND.

关键词： Domes

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Reducing Training Data Needs with Minimal Multilevel machine learning (M3L)

arXiv

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arXiv 2023年

作者： Heinen, Stefan Khan, Danish von Rudorff, Guido Falk Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Price, Alastair J.A. Nandi, Surajit Bhowmik, Arghya Hermansson, Kersti Anatole von Lilienfeld, O. Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George Campus TorontoON Canada University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Departments of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Energy Conversion and Storage DTU Anker Engelunds Vej Kgs. LyngbyDK-2800 Denmark Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZF12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9LCCSD(T), EGP, QM9CCSD(T)AE , and QM9CCSD(T)EA , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP∗(VWN5), TPSSH. © 2023, CC0.

关键词： Carbon footprint

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Rethinking Annotation Granularity for Overcoming Shortcuts in Deep learning-based Radiograph Diagnosis: A Multicenter Study

arXiv

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arXiv 2021年

作者： Luo, Luyang Chen, Hao Xiao, Yongjie Zhou, Yanning Wang, Xi Vardhanabhuti, Varut Wu, Mingxiang Han, Chu Liu, Zaiyi Fang, Xin Hao Benjamin Tsougenis, Efstratios Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong AI Research Lab Imsight Technology Co. Ltd. Shenzhen China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Radiology Shenzhen People's Hospital Shenzhen Luohu China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Radiology Queen Marry Hospital Hong Kong Artificial Intelligence Lab Head Office Information Technology and Health Informatics Division Hospital Authority Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Hong Kong

Purpose To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)-based diagnosis using chest radiographs. Materials and Methods Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34,501 chest radiographs from January 2005-September 2019 were retrospectively collected and annotated with cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The models' internal classification performance and lesion localization performance were compared on an testing set (n=2,922), external classification performance was compared on NIH-Google (n=4,376) and PadChest (n=24,536) datasets, and external lesion localization performance was compared on NIH-ChestX-ray14 dataset (n=880). The models were also compared to radiologists on a subset of the internal testing set (n=496). Performance was evaluated using receiver operating characteristic curve (ROC) analysis. Results Given sufficient training data, both models performed comparably to radiologists. CheXDet achieved significant improvement for external classification, such as in classifying fracture on NIH-Google (CheXDet area under the ROC curve [AUC]: 0.67, CheXNet AUC: 0.51;p © 2021, CC BY.

关键词： Classification (of information)

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