Institut for Elektro- og Computerteknologi

Aarhus Universitets segl

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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Vahedifar, M. A. & Zhang, Q. (2025). Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach. I 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025 IEEE Computer Society Press. https://doi.org/10.1109/MLSP62443.2025.11204284
Amer, A., Felsager, D., Brodskiy, Y. & Sarabakha, A. (2025). Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control. I International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings IEEE. https://doi.org/10.1109/IJCNN64981.2025.11228873
Carreto Picón, G., Oleksiienko, I., Hedegaard, L., Bakhtiarnia, A. & Iosifidis, A. (2026). Continual low-rank scaled dot-product attention. Neural Networks, 197, Artikel 108517. https://doi.org/10.1016/j.neunet.2025.108517
Dang, V. H., Redder, A., Pham, H. X., Sarabakha, A. & Kayacan, E. (2025). VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-to-Real Gap. IEEE Robotics and Automation Letters, 10(10), 11038-11045. https://doi.org/10.1109/LRA.2025.3606806
Mikutavicius, R., Álvarez-Tuñón, O., Brodskiy, Y. & Sarabakha, A. (2025). Towards End-to-End Underwater vSLAM Using Neural Radiance Fields. I OCEANS 2025 Brest, OCEANS 2025 IEEE. https://doi.org/10.1109/OCEANS58557.2025.11104336
Liu, S., Lang, X., Wu, J. & Rehman, N. U. (2025). Selective Noise Empirical Mode Decomposition. IEEE Signal Processing Letters, 32, 2823-2827. https://doi.org/10.1109/LSP.2025.3588082
Vahedifar, M. A., Akhtarshenas, A., Rafatpanah, M. M. & Sabbaghian, M. (2025). Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors. I 2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP) https://doi.org/10.1109/MLSP62443.2025.11204262
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), Artikel 3505. https://doi.org/10.1038/s41598-025-32761-w
Tang, F., An, X., Yang, H., Xie, Y., Yang, K., Hu, M., Cheng, Z., Zhou, X., Ran, Z., Razzak, I., Feng, Z., Bozorgtabar, B., Deng, J. & Ge, Z. (2025). Unifying Image and Video Understanding in One Vision Encoder. Afhandling præsenteret på The Thirty-ninth Annual Conference on Neural Information Processing Systems, San Diego, California, USA. https://openreview.net/pdf/46f20263cc2abbd139b0f9be3ada52e0fd7427d5.pdf
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 Bind 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, Artikel 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, Artikel 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, Artikel 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, Artikel 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. Afhandling præsenteret på 12th International Conference on Learning Representations, ICLR 2024, Hybrid, Vienna, Østrig.
Stegmüller, T., Lebailly, T., Ðukić, N., Bozorgtabar, B., Tuytelaars, T. & Thiran, J. P. (2025). A SIMPLE FRAMEWORK FOR OPEN-VOCABULARY ZERO-SHOT SEGMENTATION. I 13th International Conference on Learning Representations, ICLR 2025 (s. 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. I ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings IEEE Computer Society Press. 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. I Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (s. 7928-7938). IEEE. https://doi.org/10.1109/WACV61041.2025.00770

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Revideret 13.11.2025
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AU Engineering