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877 results on '"Woods, Adam J"'

5. Learning ratio performance on a brief visual learning and memory test moderates cognitive training gains in Double Decision task in healthy older adults

Author

Hardcastle, Cheshire, Kraft, Jessica N., Hausman, Hanna K., O’Shea, Andrew, Albizu, Alejandro, Evangelista, Nicole D., Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2024
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7. DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data., Comment: 12 pages, 5 figures, 5 tables, Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023

Published
2023

8. The impact of a tDCS and cognitive training intervention on task-based functional connectivity

Author

Kraft, Jessica N., Indahlastari, Aprinda, Boutzoukas, Emanuel M., Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, O’Shea, Andrew, Evangelista, Nicole D., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric, and Woods, Adam J.

Published
2024
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10. DOMINO: Domain-aware Loss for Deep Learning Calibration

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://github.com/lab-smile/DOMINO., Comment: 7 pages, 1 figure, 1 table, accepted by the Software Impacts journal

Published
2023

11. Towards tDCS Digital Twins Using Deep Learning-Based Direct Estimation of Personalized Electrical Field Maps from T1-Weighted MRI

Author

Stolte, Skylar E., Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Fang, Ruogu, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
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13. DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. This performance is attributed to our domain-aware regularization to inform semantic model calibration. These findings show the importance of semantic ties between class labels in building confidence in deep learning models. The framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models. The code for this article is available at: https://github.com/lab-smile/DOMINO., Comment: 10 pages, 6 figures, 3 tables. Accepted by International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Oral Talk

Published
2022

14. Connecting memory and functional brain networks in older adults: a resting-state fMRI study

Author

Waner, Jori L., Hausman, Hanna K., Kraft, Jessica N., Hardcastle, Cheshire, Evangelista, Nicole D., O’Shea, Andrew, Albizu, Alejandro, Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric C., and Woods, Adam J.

Published
2023
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16. Task-based functional connectivity of the Useful Field of View (UFOV) fMRI task

Author

Kraft, Jessica N., Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, O’Shea, Andrew, Evangelista, Nicole D., Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven, Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric, and Woods, Adam J.

Published
2023
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23. Digitalized transcranial electrical stimulation: A consensus statement

Author

Brunoni, Andre R, Ekhtiari, Hamed, Antal, Andrea, Auvichayapat, Paradee, Baeken, Chris, Benseñor, Isabela M., Bikson, Marom, Boggio, Paulo, Borroni, Barbara, Brighina, Filippo, Brunelin, Jerome, Carvalho, Sandra, Caumo, Wolnei, Ciechanski, Patrick, Charvet, Leigh, Clark, Vincent P., Cohen Kadosh, Roi, Cotelli, Maria, Datta, Abhishek, Deng, Zhi-De, De Raedt, Rudi, De Ridder, Dirk, Fitzgerald, Paul B., Floel, Agnes, Frohlich, Flavio, George, Mark S., Ghobadi-Azbari, Peyman, Goerigk, Stephan, Hamilton, Roy H., Jaberzadeh, Shapour J., Hoy, Kate, Kidgell, Dawson J., Zonoozi, Arash Khojasteh, Kirton, Adam, Laureys, Steven, Lavidor, Michal, Lee, Kiwon, Leite, Jorge, Lisanby, Sarah H., Loo, Colleen, Martin, Donel M., Miniussi, Carlo, Mondino, Marine, Monte-Silva, Katia, Morales-Quezada, Leon, Nitsche, Michael A., Okano, Alexandre H., Oliveira, Claudia S., Onarheim, Balder, Pacheco-Barrios, Kevin, Padberg, Frank, Nakamura-Palacios, Ester M., Palm, Ulrich, Paulus, Walter, Plewnia, Christian, Priori, Alberto, Rajji, Tarek K., Razza, Lais B., Rehn, Erik M., Ruffini, Giulio, Schellhorn, Klaus, Zare-Bidoky, Mehran, Simis, Marcel, Skorupinski, Pawel, Suen, Paulo, Thibaut, Aurore, Valiengo, Leandro C.L., Vanderhasselt, Marie-Anne, Vanneste, Sven, Venkatasubramanian, Ganesan, Violante, Ines R., Wexler, Anna, Woods, Adam J., and Fregni, Felipe

Published
2022
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24. Does transcranial direct current stimulation enhance cognitive performance in Parkinson’s disease mild cognitive impairment? An event-related potentials and neuropsychological assessment study

Author

Aksu, Serkan, Uslu, Atilla, İşçen, Pınar, Tülay, Emine Elif, Barham, Huzeyfe, Soyata, Ahmet Zihni, Demirtas-Tatlidede, Asli, Yıldız, Gülsen Babacan, Bilgiç, Başar, Hanağası, Haşmet, Woods, Adam J., Karamürsel, Sacit, and Uyar, Fatma Aytül

Published
2022
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25. Higher white matter hyperintensity load adversely affects pre-post proximal cognitive training performance in healthy older adults

Author

Boutzoukas, Emanuel M., O’Shea, Andrew, Kraft, Jessica N., Hardcastle, Cheshire, Evangelista, Nicole D., Hausman, Hanna K., Albizu, Alejandro, Van Etten, Emily J., Bharadwaj, Pradyumna K., Smith, Samantha G., Song, Hyun, Porges, Eric C., Hishaw, Alex, DeKosky, Steven T., Wu, Samuel S., Marsiske, Michael, Alexander, Gene E., Cohen, Ronald, and Woods, Adam J.

Published
2022
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26. Proximal improvement and higher-order resting state network change after multidomain cognitive training intervention in healthy older adults

Author

Hardcastle, Cheshire, Hausman, Hanna K., Kraft, Jessica N., Albizu, Alejandro, O’Shea, Andrew, Boutzoukas, Emanuel M., Evangelista, Nicole D., Langer, Kailey, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2022
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27. Cingulo-opercular and frontoparietal control network connectivity and executive functioning in older adults

Author

Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, Kraft, Jessica N., Evangelista, Nicole D., Boutzoukas, Emanuel M., Langer, Kailey, O’Shea, Andrew, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2022
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28. The association between head motion during functional magnetic resonance imaging and executive functioning in older adults

Author

Hausman, Hanna K., Hardcastle, Cheshire, Kraft, Jessica N., Evangelista, Nicole D., Boutzoukas, Emanuel M., O'Shea, Andrew, Albizu, Alejandro, Langer, Kailey, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, Hishaw, Georg A., Wu, Samuel, DeKosky, Steven, Alexander, Gene E., Marsiske, Michael, Cohen, Ronald, and Woods, Adam J.

Published
2022
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31. Global Deterioration Scale

Author

Hardcastle, Cheshire, Taylor, Brad, Price, Catherine, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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32. Geriatric Mental Health

Author

Khan, Jessica, Holbert, Richard, Averbuch, Robert, Suryadevara, Uma, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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33. Electrophysiology

Author

Dominguez, Vanessa, Woods, Adam J., Woods¹, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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36. Cognitive Processes

Author

Chung, Christie, Lin, Ziyong, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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39. Central Nervous System

Author

Taylor, Brad, Hardcastle, Cheshire, Marsiske, Michael, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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40. Cerebral Metabolism

Author

Fernando, Heshan J., Bowers, Dawn, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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43. Vascular Dementia

Author

Wiggins, Margaret E., Price, Catherine, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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44. Validation Therapy

Author

Belser-Ehrlich, Janna, Bowers, Dawn, Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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50. Alzheimer’s Disease

Author

Shea, Yat-Fung, DeKosky, Steven T., Woods, Adam J., Section editor, Gu, Danan, editor, and Dupre, Matthew E., editor

Published
2021
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