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- Anjomshoae, Sule, 1985-, et al.
(författare)
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Explaining graph convolutional network predictions for clinicians : an explainable AI approach to Alzheimer’s disease classification
- 2024
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification.Methods: Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level.Results: Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations.Discussion: Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.
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| 2. |
- Cavalcanti, Julio Cesar, et al.
(författare)
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Exploring the performance of automatic speaker recognition using twin speech and deep learning-based artificial neural networks
- 2024
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Ingår i: Frontiers in Artificial Intelligence. - 2624-8212. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- This study assessed the influence of speaker similarity and sample length on the performance of an automatic speaker recognition (ASR) system utilizing the SpeechBrain toolkit. The dataset comprised recordings from 20 male identical twin speakers engaged in spontaneous dialogues and interviews. Performance evaluations involved comparing identical twins, all speakers in the dataset (including twin pairs), and all speakers excluding twin pairs. Speech samples, ranging from 5 to 30 s, underwent assessment based on equal error rates (EER) and Log cost-likelihood ratios (Cllr). Results highlight the substantial challenge posed by identical twins to the ASR system, leading to a decrease in overall speaker recognition accuracy. Furthermore, analyses based on longer speech samples outperformed those using shorter samples. As sample size increased, standard deviation values for both intra and inter-speaker similarity scores decreased, indicating reduced variability in estimating speaker similarity/dissimilarity levels in longer speech stretches compared to shorter ones. The study also uncovered varying degrees of likeness among identical twins, with certain pairs presenting a greater challenge for ASR systems. These outcomes align with prior research and are discussed within the context of relevant literature.
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| 4. |
- Karlsson, Kalle
(författare)
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Exploring the surveillance technology discourse : a bibliometric analysis and topic modeling approach
- 2024
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Ingår i: Frontiers in Artificial Intelligence. - Switzerland : Frontiers Media S.A.. - 2624-8212. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- The prevention of crime is a multifaceted challenge with legal, political, and cultural implications. Surveillance technologies play a crucial role in assisting law enforcement and other relevant parties in this mission. Drones, cameras, and wiretaps are examples of such devices. As their use increases, it becomes essential to address related challenges involving various stakeholders and consider cultural, political, and legal aspects. The objective of this study was to analyze the impact of surveillance technologies and identify commonalities and differences in perspectives among social media users and researchers. Data extraction was performed from two platforms: Scopus (for academic research papers) and platform X (formerly known as Twitter). The dataset included 88,989 tweets and 4,874 research papers. Topic modeling, an unsupervised machine learning approach, was applied to analyze the content. The research results revealed that privacy received little attention across the datasets, indicating its relatively low prominence. The military applications and their usage have been documented in academic research articles as well as tweets. Based on the empirical evidence, it seems that contemporary surveillance technology may be accurately described as possessing a bi-directional nature, including both sousveillance and surveillance, which aligns with Deleuzian ideas on the Panopticon. The study’s findings also indicate that there was a greater level of interest in actual applications of surveillance technologies as opposed to more abstract concepts like ethics and privacy.
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