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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Alonso-Fernandez, Fernando, et al.
(författare)
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Fusion of static image and dynamic information for signature verification
- 2009
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Ingår i: ICIP 2009. - Piscataway, N.J. : IEEE Press. - 9781424456543 ; , s. 2725-2728
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Konferensbidrag (refereegranskat)abstract
- This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.
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| 3. |
- Etminani, Kobra, 1984-, et al.
(författare)
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A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
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| 4. |
- Johannes, A., et al.
(författare)
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Enhanced sputtering and incorporation of Mn in implanted GaAs and ZnO nanowires
- 2014
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Ingår i: Journal of Physics D: Applied Physics. - Bristol : IOP Publishing. - 1361-6463 .- 0022-3727. ; 47:39
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Tidskriftsartikel (refereegranskat)abstract
- We simulated and experimentally investigated the sputter yield of ZnO and GaAs nanowires, which were implanted with energetic Mn ions at room temperature. The resulting thinning of the nanowires and the dopant concentration with increasing Mn ion fluency were measured by accurate scanning electron microscopy (SEM) and nano-x-Ray Fluorescence (nanoXRF) quantification, respectively. We observed a clearly enhanced sputter yield for the irradiated nanowires compared to bulk, which is also corroborated by iradina simulations. These show a maximum if the ion range matches the nanowire diameter. As a consequence of the erosion thinning of the nanowire, the incorporation of the Mn dopants is also enhanced and increases non-linearly with increasing ion fluency.
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| 5. |
- Laber, Christien P., et al.
(författare)
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Seasonal and Spatial Variations in Synechococcus Abundance and Diversity Throughout the Gullmar Fjord, Swedish Skagerrak
- 2022
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Ingår i: Frontiers in Microbiology. - Lausanne : Frontiers Media S.A.. - 1664-302X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- The picophytoplankton Synechococcus is a globally abundant autotroph that contributes significantly to primary production in the oceans and coastal areas. These cyanobacteria constitute a diverse genus of organisms that have developed independent niche spaces throughout aquatic environments. Here, we use the 16S V3-V4 rRNA gene region and flow cytometry to explore the diversity of Synechococcus within the picophytoplankton community in the Gullmar Fjord, on the west coast of Sweden. We conducted a station-based 1-year time series and two transect studies of the fjord. Our analysis revealed that within the large number of Synechococcus amplicon sequence variants (ASVs; 239 in total), prevalent ASVs phylogenetically clustered with clade representatives in both marine subcluster 5.1 and 5.2. The near-surface composition of ASVs shifted from spring to summer, when a 5.1 subcluster dominated community developed along with elevated Synechococcus abundances up to 9.3 x 10(4) cells ml(-1). This seasonal dominance by subcluster 5.1 was observed over the length of the fjord (25 km), where shifts in community composition were associated with increasing depth. Unexpectedly, the community shift was not associated with changes in salinity. Synechococcus abundance dynamics also differed from that of the photosynthetic picoeukaryote community. These results highlight how seasonal variations in environmental conditions influence the dynamics of Synechococcus clades in a high latitude threshold fjord.
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| 6. |
- Martinez-Diaz, M., et al.
(författare)
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Hill-climbing and brute-force attacks on biometric systems: A case study in Match-on-Card fingerprint verification
- 2006
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Ingår i: Proceedings 2006. - Piscataway, N.J. : IEEE Press. - 9781424401741 ; , s. 151-159
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study the robustness of state-of-theart automatic fingerprint verification systems against hill-climbing and brute-force attacks. We compare the performance of this type of attacks against two different minutiae-based systems, the NIST Fingerprint Image Software 2 (NFIS2) reference system and a Match-on-Card based system. In order to study their success rate, the attacks are analyzed and modified in each scenario. We focus on the influence of initial conditions in hill-climbing attacks, like the number of minutiae in the synthetically generated templates or the performance of each type of modification in the template. We demonstrate how slight modifications in the hill-climbing algorithm lead to very different success rates. © 2006 IEEE.
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| 7. |
- Soliman, Amira, 1980-, et al.
(författare)
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Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model
- 2022
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Ingår i: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 22, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones. © 2022, The Author(s).
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