| 1. |
- Olczak, Jakub, et al.
(författare)
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Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders : an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal
- 2021
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Ingår i: Acta Orthopaedica. - : Taylor & Francis. - 1745-3674 .- 1745-3682. ; 92:5, s. 513-525
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.
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| 2. |
- Pavlopoulos, Ioannis, 1983-, et al.
(författare)
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Automotive fault nowcasting with machine learning and natural language processing
- 2024
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565. ; 113:2, s. 843-861
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Tidskriftsartikel (refereegranskat)abstract
- Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, most AI-based prognostics and health management in the automotive industry ignore textual descriptions of the experienced problems or symptoms. With this study, however, we propose an ML-assisted workflow for automotive fault nowcasting that improves on current industry standards. We show that a multilingual pre-trained Transformer model can effectively classify the textual symptom claims from a large company with vehicle fleets, despite the task’s challenging nature due to the 38 languages and 1357 classes involved. Overall, we report an accuracy of more than 80% for high-frequency classes and above 60% for classes with reasonable minimum support, bringing novel evidence that automotive troubleshooting management can benefit from multilingual symptom text classification.
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| 3. |
- Bosson, J. K., et al.
(författare)
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Psychometric Properties and Correlates of Precarious Manhood Beliefs in 62 Nations
- 2021
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Ingår i: Journal of Cross-Cultural Psychology. - : SAGE Publications. - 0022-0221 .- 1552-5422. ; 52:3
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Tidskriftsartikel (refereegranskat)abstract
- Precarious manhood beliefs portray manhood, relative to womanhood, as a social status that is hard to earn, easy to lose, and proven via public action. Here, we present cross-cultural data on a brief measure of precarious manhood beliefs (the Precarious Manhood Beliefs scale [PMB]) that covaries meaningfully with other cross-culturally validated gender ideologies and with country-level indices of gender equality and human development. Using data from university samples in 62 countries across 13 world regions (N = 33,417), we demonstrate: (1) the psychometric isomorphism of the PMB (i.e., its comparability in meaning and statistical properties across the individual and country levels); (2) the PMB's distinctness from, and associations with, ambivalent sexism and ambivalence toward men; and (3) associations of the PMB with nation-level gender equality and human development. Findings are discussed in terms of their statistical and theoretical implications for understanding widely-held beliefs about the precariousness of the male gender role.
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| 4. |
- Boulieris, Petros, et al.
(författare)
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Fraud detection with natural language processing
- 2023
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565.
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Tidskriftsartikel (refereegranskat)abstract
- Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP, the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods.
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| 5. |
- Chatzipanagiotou, Marita, et al.
(författare)
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Automated recognition of geographical named entities in titles of Ukiyo-e prints
- 2021
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Ingår i: Journal of the ACM / Association for Computing Machinery. - New York, NY, USA : ACM. - 0004-5411. ; , s. 70-77
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the application of Natural Language Processing as a means to study the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints. We introduce a new dataset with titles of landscape prints that have been annotated by an art historian for any included place-names. The prints are hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto, one of the hubs of Digital Humanities in Japan. By applying, calibrating and assessing a Named Entity Recognition (NER) tool, we argue that ‘distant viewing’ or macroanalysis of visual datasets can be facilitated, which is needed to assist art historical studies of this rich, complex and diverse research material. Experimental results indicated that the performance of NER can be improved by 30% and reach 50% precision, by using part of the introduced dataset.
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| 6. |
- Dritsa, Konstantina, et al.
(författare)
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A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
- 2022
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Konferensbidrag (refereegranskat)abstract
- Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as g In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files. We explain how it was constructed and the challenges that we had to overcome. The dataset can be used for both computational linguistics and political analysis—ideally, combining the two. We present such an application, showing (i) how the dataset can be used to study the change of word usage through time, (ii) between significant historical events and political parties, (iii) by evaluating and employing algorithms for detecting semantic shifts.
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| 7. |
- Karatzas, Basil, et al.
(författare)
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AUEB NLP Group at ImageCLEFmed Caption 2020
- 2020
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Ingår i: CEUR Workshop Proceedings.
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Konferensbidrag (refereegranskat)abstract
- This article concerns the participation of AUEB’s NLP Group in the ImageCLEFmed Caption task of 2020. The goal of the task was to identify medical terms that best describe each image, in order to accelerate and improve the interpretation of medical images by experts and systems. The systems we implemented extend our previous work [7,8,9] on models that employ CNN image encoders combined with an image retrieval method or a feed-forward neural network. Our systems were ranked 1st, 2nd and 6th.
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| 8. |
- Kougia, Vasiliki, et al.
(författare)
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Medical Image Tagging by Deep Learning and Retrieval
- 2020
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Ingår i: Experimental IR Meets Multilinguality, Multimodality, and Interaction. - Cham : Springer. - 9783030582180 - 9783030582197 ; , s. 154-166
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Konferensbidrag (refereegranskat)abstract
- Radiologists and other qualified physicians need to examine and interpret large numbers of medical images daily. Systems that would help them spot and report abnormalities in medical images could speed up diagnostic workflows. Systems that would help exploit past diagnoses made by highly skilled physicians could also benefit their more junior colleagues. A task that systems can perform towards this end is medical image classification, which assigns medical concepts to images. This task, called Concept Detection, was part of the ImageCLEF 2019 competition. We describe the methods we implemented and submitted to the Concept Detection 2019 task, where we achieved the best performance with a deep learning method we call ConceptCXN. We also show that retrieval-based methods can perform very well in this task, when combined with deep learning image encoders. Finally, we report additional post-competition experiments we performed to shed more light on the performance of our best systems. Our systems can be installed through PyPi as part of the BioCaption package.
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| 9. |
- Lagkiou, Konstantina, et al.
(författare)
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Distant Viewing of Ukiyo-e Prints
- 2022
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Ingår i: Proceedings of the Thirteenth Language Resources and Evaluation Conference. - Marseilles : European Language Resources Association. ; , s. 5879-5888
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Konferensbidrag (refereegranskat)abstract
- This paper contributes to studying relationships between Japanese topography and places featured in early modern landscape prints, so-called ukiyo-e or ‘pictures of the floating world’. The printed inscriptions on these images feature diverse place-names, both man-made and natural formations. However, due to the corpus’s richness and diversity, the precise nature of artistic mediation of the depicted places remains little understood. In this paper, we explored a new analytical approach based on the macroanalysis of images facilitated by Natural Language Processing technologies. This paper presents a small dataset with inscriptions on prints that have been annotated by an art historian for included place-name entities. Our dataset is released for public use. By fine-tuning and applying a Japanese BERT-based Name Entity Recogniser, we provide a use-case of a macroanalysis of a visual dataset that is hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto. Our work studies the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints, demonstrating how an art historian’s work can be improved with Natural Language Processing toward distant viewing of visual datasets.
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| 10. |
- Pavlopoulos, John, et al.
(författare)
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Diagnostic captioning : a survey
- 2022
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Ingår i: Knowledge and Information Systems. - : Springer Science and Business Media LLC. - 0219-1377 .- 0219-3116. ; 64:7, s. 1691-1722
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Tidskriftsartikel (refereegranskat)abstract
- Diagnostic captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination. DC can assist inexperienced physicians, reducing clinical errors. It can also help experienced physicians produce diagnostic reports faster. Following the advances of deep learning, especially in generic image captioning, DC has recently attracted more attention, leading to several systems and datasets. This article is an extensive overview of DC. It presents relevant datasets, evaluation measures, and up-to-date systems. It also highlights shortcomings that hinder DC’s progress and proposes future directions.
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