Department of Electrical and Computer Engineering

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 Department of Electrical and Computer Engineering Research Key areas in research & development Signal Processing & Machine Learning Publications

Publications by Signal Processing & Machine Learning

Are you looking for publications by Section of Signal Processing & Machine Learning? On this page you can find all the publications made by the Section of Signal Processing & Machine Learning - Department of Electrical and Computer Engineering, Aarhus University.

Below you can find a list of all the publications, their publishing date, their author(s), and titles. The list can be sorted by date, author, and title:

List of Publications

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Asif, M., Sovacool, B. K., Ali, Z., Heinz, E., Kwan, T. A., Nordensväed, J., Krishna, A., Thollander, P., Rohdin, P. & Zhang, W. (2026). Of demographics, technology, and geography: The social determinants of energy consumption patterns and user behaviour in Saudi Arabia’s residential sector. Energy and Buildings, 356, Article 117061. https://doi.org/10.1016/j.enbuild.2026.117061
Akbar, H., Abro, G. E. M., Baloch, S. K., Khan, T. A., Memon, I., Nasir, H. & Memon, S. A. (2026). Exploring carbon nanotube-copper composites for enhanced induction motor design in electrical vehicles. Scientific Reports, 16(1), Article 3505. https://doi.org/10.1038/s41598-025-32761-w
Bozorgtabar, B., Mahapatra, D., von Teng, H., Pollinger, A., Ebner, L., Thiran, J.-P. & Reyes, M. (2019). Informative sample generation using class aware generative adversarial networks for classification of chest Xrays.
Lebailly, T., Stegmüller, T., Bozorgtabar, B., Thiran, J.-P. & Tuytelaars, T. (2023). Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning.
Rad, M. S., Bozorgtabar, B., Musat, C., Marti, U.-V., Basler, M., Ekenel, H. K. & Thiran, J.-P. (2019). Benefiting from Multitask Learning to Improve Single Image Super-Resolution.
Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection.
Pati, P., Jaume, G., Ayadi, Z., Thandiackal, K., Bozorgtabar, B., Gabrani, M. & Goksel, O. (2023). Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer.
Rad, M. S., Bozorgtabar, B., Marti, U.-V., Basler, M., Ekenel, H. K. & Thiran, J.-P. (2019). Srobb: Targeted Perceptual Loss for Single Image Super-Resolution.
Rad, M. S., Yu, T., Musat, C., Ekenel, H. K., Bozorgtabar, B. & Thiran, J.-P. (2020). Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution.
Anklin, V., Pati, P., Jaume, G., Bozorgtabar, B., Foncubierta-Rodríguez, A., Thiran, J.-P., Sibony, M., Gabrani, M. & Goksel, O. (2021). Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs.
Jaume, G., Pati, P., Bozorgtabar, B., Foncubierta-Rodríguez, A., Feroce, F., Anniciello, A. M., Rau, T., Thiran, J.-P., Gabrani, M. & Goksel, O. (2020). Quantifying Explainers of Graph Neural Networks in Computational Pathology.
Rad, M. S., Yu, T., Bozorgtabar, B. & Thiran, J.-P. (2021). Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images.
Stegmüller, T., Lebailly, T., Bozorgtabar, B., Tuytelaars, T. & Thiran, J.-P. (2023). CrOC: Cross-View Online Clustering for Dense Visual Representation Learning.
Jaume, G., Bozorgtabar, B., Ekenel, H. K., Thiran, J.-P. & Gabrani, M. (2018). Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks.
Li, C., Bozorgtabar, B., Ping, Y., Huang, P. & Qin, J. (2025). Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation.
Abbet, C., Zlobec, I., Bozorgtabar, B. & Thiran, J.-P. (2020). Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Shao, L. (2020). Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Reyes, M. (2018). Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Shao, L. (2020). Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance.
Mahapatra, D. & Bozorgtabar, B. (2019). Progressive Generative Adversarial Networks for Medical Image Super resolution.
Mahapatra, D. & Bozorgtabar, B. (2017). Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution.
Tomar, D., Lortkipanidze, M., Vray, G., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation. https://doi.org/10.1109/TMI.2021.3059265
Vray, G., Tomar, D., Gao, X., Thiran, J.-P., Shelhamer, E. & Bozorgtabar, B. (2025). ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains. Curran Associates, Inc. Advances in Neural Information Processing Systems Vol. 38
Bozorgtabar, B., Rad, M. S., Ekenel, H. K. & Thiran, J.-P. (2019). Learn to synthesize and synthesize to learn.
Bozorgtabar, B., Rad, M. S., Ekenel, H. K. & Thiran, J.-P. (2019). Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis.
Bozorgtabar, B., Mahapatra, D., Vray, G. & Thiran, J.-P. (2020). Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning.
Tomar, D., Bozorgtabar, B., Lortkipanidze, M., Vray, G., Rad, M. S. & Thiran, J.-P. (2021). Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation.
Tomar, D., Vray, G., Bozorgtabar, B. & Thiran, J.-P. (2023). TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation.
Deria, A., Mahapatra, D., Bozorgtabar, B., Chakraborty, M., Chakraborty, S. & Roy, S. (2025). MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization.
Stegmüller, T., Bozorgtabar, B., Spahr, A. & Thiran, J.-P. (2022). ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification.
Bozorgtabar, B., Mahapatra, D. & Thiran, J.-P. (2023). Amae: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays.
Spahr, A., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays.
Mahapatra, D., Tennakoon, R., George, Y., Roy, S., Bozorgtabar, B., Ge, Z. & Reyes, M. (2024). ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification. Medical Image Analysis, 97, Article 103261. https://doi.org/10.1016/j.media.2024.103261
Mahapatra, D., Jimeno Yepes, A., Bozorgtabar, B., Roy, S., Ge, Z. & Reyes, M. (2025). Multi-Label Generalized Zero Shot Chest X-Ray Classification by Combining Image-Text Information With Feature Disentanglement. IEEE Transactions on Medical Imaging, 44(1), 31-43. https://doi.org/10.1109/TMI.2024.3429471
Mahapatra, D., Bozorgtabar, B., Ge, Z. & Reyes, M. (2024). GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification. Medical Image Analysis, 93, Article 103075. https://doi.org/10.1016/j.media.2023.103075
Chakraborty, S., Kumar, K., Deria, A., Mahapatra, D., Bozorgtabar, B. & Roy, S. (2025). Robust semantic learning for precise medical image segmentation. Biomedical Signal Processing and Control, 110, Article 108251. https://doi.org/10.1016/j.bspc.2025.108251
Stegmüller, T., Abbet, C., Bozorgtabar, B., Clarke, H., Petignat, P., Vassilakos, P. & Thiran, J. P. (2024). Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime. Computers in Biology and Medicine, 169, Article 107809. https://doi.org/10.1016/j.compbiomed.2023.107809
Mahapatra, D., Bozorgtabar, B., Ge, Z., Reyes, M. & Thiran, J. P. (2024). Combining Graph Transformers Based Multi-Label Active Learning and Informative Data Augmentation for Chest Xray Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 38(19), 21378-21386. https://doi.org/10.1609/aaai.v38i19.30133
Tomar, D., Vray, G., Thiran, J. P. & Bozorgtabar, B. (2024). UN-MIXING TEST-TIME NORMALIZATION STATISTICS: COMBATTING LABEL TEMPORAL CORRELATION. Paper presented at 12th International Conference on Learning Representations, ICLR 2024, Hybrid, Vienna, Austria.
Stegmüller, T., Lebailly, T., Ðukić, N., Bozorgtabar, B., Tuytelaars, T. & Thiran, J. P. (2025). A SIMPLE FRAMEWORK FOR OPEN-VOCABULARY ZERO-SHOT SEGMENTATION. In 13th International Conference on Learning Representations, ICLR 2025 (pp. 72821-72842)
Vray, G., Tomar, D., Bozorgtabar, B. & Thiran, J. P. (2024). Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology. IEEE Transactions on Medical Imaging, 43(5), 2021-2032. https://doi.org/10.1109/TMI.2024.3355645
Tomar, D., Vray, G., Mahapatra, D., Roy, S., Thiran, J. P. & Bozorgtabar, B. (2025). Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology. In ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings IEEE Computer Society. https://doi.org/10.1109/ISBI60581.2025.10980748
Kumar, K., Chakraborty, S., Mahapatra, D., Bozorgtabar, B. & Roy, S. (2025). Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 7928-7938). IEEE. https://doi.org/10.1109/WACV61041.2025.00770
Lebailly, T., Stegmüller, T., Bozorgtabar, B., Thiran, J. P. & Tuytelaars, T. (2024). CRIBO: SELF-SUPERVISED LEARNING VIA CROSS-IMAGE OBJECT-LEVEL BOOTSTRAPPING. Paper presented at 12th International Conference on Learning Representations, ICLR 2024, Hybrid, Vienna, Austria.
Kass, M. A., Ruganiza, A. K., Grombacher, D., Vela, I., Elias, R., Ondicho, A., Griffiths, M. P. & Pedersen, J. B. (2025). Transient Electromagnetic Surveys for Groundwater Investigations Near the Kakuma Refugee Camp, Kenya. https://doi.org/10.3997/2214-4609.202520072
Gebreyesus, G., Nawoya, S., Ssemakula, F., Karstoft, H., Bjerge, K., Kunyanga, C. N., Mwikirize, C., Akol, R., Katumba, A., Nakimbugwe, D. & Geissmann, Q. (2024). Innovative phenotyping systems to advance selective breeding in black soldier fly: status from the FlyGene project. Journal of Insects as Food and Feed, 10(Supplement 1 (2024) S1–S353), S250. Article Volume 10, Supplement 1 (2024). https://doi.org/10.1163/23524588-20241013
Nazari, M., Korshoej, A. R. & Rehman, N. U. (2025). Successive Jump and Mode Decomposition. https://arxiv.org/abs/2504.08453
Gaikwad, N. B., Liu, L., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2025). A high-duty-cycle transmitter unit for steady-state surface NMR instruments. Geoscientific Instrumentation, Methods and Data Systems, 14(2), 139-151. https://doi.org/10.5194/gi-14-139-2025
Naveed, K. & Rehman, N. U. (2025). A Fully Multivariate Multifractal Detrended Fluctuation Analysis Method for Fault Diagnosis.
Lillelund, C. M., Kalra, S. & Greiner, R. (2025). A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis. PLoS One, 20(11 ), Article e0336476. https://doi.org/10.1371/journal.pone.0336476

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