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Filters: Keyword is deep learning  [Clear All Filters]
2023
A. H. Z. Nik, M. Riegler, P. Halvorsen and A. Storås. Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks In International conference on multimedia modeling. Springer International Publishing, 2023.
2022
F. I. Michelinakis, J. S. Pujol-Roig, S. Malacarne, M. Xie, T. Dreibholz, S. Majumdar, W. Y. Poe, G. Patounas, C. Guerrero, A. Elmokashfi et al. "AI Anomaly Detection for Cloudified Mobile Core Architectures." Transactions on Network and Service Management (2022).PDF icon tnsm2022.pdf (9.43 MB)
E. J. Husom, P. Bernabé and S. Sen. "Deep learning to predict power output from respiratory inductive plethysmography data." Applied AI Letters 3, no. 2 (2022): e65.
2021
N. K. Tomar, D. Jha, S. Ali, H. D. Johansen, D. Johansen, M. Riegler and P. Halvorsen. DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation In 25th International Conference on Pattern Recognition. Springer, 2021.PDF icon endotect_segmentation_debesh.pdf (4.12 MB)
B. Singstad, E. M. Muten and P. H. Brekke. Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain In 2021 Computing in Cardiology (CinC)2021 Computing in Cardiology (CinC), Edited by C. Pickett. Brno, Czech Republic: IEEE, 2021.
D. Jha, N. K. Tomar, S. Ali, M. Riegler, H. D. Johansen, D. Johansen, T. de Lange and P. Halvorsen. NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy In 34th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2021.PDF icon 412100a037.pdf (789.29 KB)
K. Sahinbas and F. O. Catak. "Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images." In Data Science for COVID-19, edited by U. Kose, D. Gupta, V. H. C. de Albuquerque and A. Khanna, 451-466. Academic Press, 2021.PDF icon 3-s2.0-b9780128245361000034-main.pdf (2.1 MB)
2020
B. Singstad and C. Tronstad. Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs In 2020 Computing in Cardiology Conference2020 Computing in Cardiology Conference (CinC), Edited by C. Pickett. Computing in Cardiology, 2020.
A. Nawaz, Z. Huang, S. Wang and A. Naseer. Deep neural architecture for geospatial trajectory completion over occupancy gridmap In 2020 IEEE 13th International Conference on Cloud Computing (CLOUD)2020 IEEE 13th International Conference on Cloud Computing (CLOUD), Edited by L. O’Conner. Beijing, China: IEEE, 2020.PDF icon amaranaseer_paper2020.pdf (180.26 KB)
2019
D. Jha, P. H. Smedsrud, M. Riegler, D. Johansen, T. de Lange, P. Halvorsen and H. D. Johansen. ResUNet++: An Advanced Architecture for Medical Image Segmentation In 2019 IEEE International Symposium on Multimedia (ISM). San Diego, California, USA: IEEE, 2019.PDF icon resunet_accepted_debesh.pdf (1.01 MB)
2018
K. Pogorelov, O. Ostroukhova, A. Petlund, P. Halvorsen, T. de Lange, H. Espeland, T. Kupka, C. Griwodz and M. Riegler. Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia In 2018 IEEE Conference on Biomedical and Health Informatics (BHI). IEEE, 2018.
S. Hicks, P. H. Smedsrud, P. Halvorsen and M. Riegler. Deep Learning Based Disease Detection Using Domain Specific Transfer Learning In MediaEval 2018. MediaEval, 2018.PDF icon mediaeval_2018_working_notes_paper.pdf (366.65 KB)
R. Borgli. Hyperparameter optimization using Bayesian optimization on transfer learning for medical image classification In The University of Oslo., 2018.PDF icon master-thesis.pdf (20.39 MB)
V. Thambawita, D. Jha, M. Riegler, P. Halvorsen, H. L. Hammer, H. D. Johansen and D. Johansen. The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning In MediaEval 2018. Nice, France: MediaEval, 2018.PDF icon mediaeval_team_dv_10.pdf (374.73 KB)