Publikationen von L Mahler

Birk, F.; Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP. Scientific Reports 15 (1), 4825 (2025)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning. In: IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, S. 2452 - 2461. 2nd Workshop on Computer Vision for Automated Medical Diagnosis: International Conference on Computer Vision (ICCV/CVF 2023) , Paris, France, 02. Oktober 2023 - 06. Oktober 2023. (2023)

Mahler, L.; Wang, Q.; Steiglechner, J.; Birk, F.; Heczko, S.; Scheffler, K.; Lohmann, G.: Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, S. 123 - 132 (Hg. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, 08. Oktober 2023. Springer, Cham, Switzerland (2023)

Wang, Q.; Mahler, L.; Steiglechner, J.; Birk, F.; Scheffler, K.; Lohmann, G.: A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging. In: Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, S. 23 - 33 (Hg. Abdulkadir, A.; Bathula, D.; Dvornek, N.; Govindarajan, S.; Habes, M. et al.). 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023), Vancouver, BC, Canada, 08. Oktober 2023. Springer, Cham, Switzerland (2023)

Steiglechner, J.; Wang, Q.; Ramadan, D.; Mahler, L.; Scheffler, K.; Bender, B.; Lindig, T.; Lohmann, G.: FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T. In: Medical Imaging with Deep Learning (MIDL 2022). Medical Imaging with Deep Learning (MIDL 2022), Zürich, Swtzerland, 06. Juli 2022 - 08. Juli 2022. (2022)

Birk, F.; Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Distribution-free uncertainty estimation in multi-parametric quantitative MRI through conformalized quantile regression. In ISMRM & ISMRT Annual Meeting & Exhibition 2024, 2228. ISMRM & ISMRT Annual Meeting & Exhibition 2024, Singapore, 04. Mai 2024 - 09. Mai 2024. (2024)

Mahler, L.; Scheffler, K.; Lohmann, G.: Quantitative Assessment of ML-Based Feature Attribution Methods for ASD Biomarker Discovery in fMRI. 30th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2024), Seoul, South Korea (2024)

Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Lohmann, G.: JudgeMI: Towards Accurate Metrics for Assessing Deep Learning Based Structural MRI Motion Correction. 29th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2023), Montreal, Canada (2023)

Mahler, L.: Spatio-Temporal Feature Extraction for Action Recognition in Videos. Bachelor, Technische Hochschule Ulm, Ulm, Germany (2020)

Mahler, L.; Steiglechner, J.; Bender, B.; Lindig, T.; Ramadan, D.; Bause, J.; Birk, F.; Heule, R.; Charyasz, E.; Erb, M. et al.; Kumar, V.; Hagberg, G.; Martin, P.; Lohmann, G.; Scheffler, K.: UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations. (eingereicht)

Mahler, L.; Steiglechner, J.; Wang, Q.; Scheffler, K.; Heule, R.: Flexible and Cost-Effective Deep Learning for Fast Multi-Parametric Relaxometry using Phase-Cycled bSSFP. (eingereicht)

Lohmann, G.; Heczko, S.; Mahler, L.; Wang, Q.; Steiglechner, J.; Kumar, V.; Roost, M.; Jost, J.; Scheffler, K.: Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning. (eingereicht)