Google Scholar : my papers gathered over 5400 citations and I have an h-index of 22.

Preprints

  • Modeling disease progression in spinocerebellar ataxias, E Georgii, et al., medRxiv, 2024.
  • DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties, Oestreich M, Merdivan E, Lee M, Schultze JL, Piraud M, Becker M, bioRxiv, 2024.
  • TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks, van der Weg K, Merdivan E, Piraud M, Gohlke H, bioRxiv, 2024.
  • Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting, Durrer A, , et al., arXiv, 2024.
  • Multimodal imaging and deep learning unveil pulmonary delivery profiles and acinar migration of tissue-resident macrophages in the lung, Yang L, et al., Research Square, 2023.
  • Panoptica--instance-wise evaluation of 3D semantic and instance segmentation maps, F. Kofler, H. Möller, J. A. Buchner, E. de la Rosa, I. Ezhov, M. Rosier, I. Mekki, S. Shit, M. Negwer, R. Al-Maskari, A. Ertürk, S. Vinayahalingam, F. Isensee, S. Pati, D. Rueckert, J. S. Kirschke, S. K. Ehrlich, A. Reinke, B. Menze, B. Wiestler, M. Piraud, arXiv, 2023.
  • Framing image registration as a landmark detection problem for better representation of clinical relevance, D. Waldmannstetter, B. Wiestler, J. Schwarting, I. Ezhov, M. Metz, S. Bakas, B. Baheti, S. Chakrabarty, J. S. Kirschke, R. A. Heckemann, M. Piraud, F. Kofler, B. Menze, arXiv, 2023.
  • Primitive Simultaneous Optimization of Similarity Metrics for Image Registration, D. Waldmannstetter, B. Wiestler, J. Schwarting, I. Ezhov, M. Metz, S. Bakas, B. Baheti, S. Chakrabarty, D. Rueckert, J. S. Kirschke, R. A. Heckemann, M. Piraud, B. Menze, F. Kofler, arXiv, 2023.
  • Probe set selection for targeted spatial transcriptomics, L. B. Kuemmerle, M. D. Luecken, A. B. Firsova, L. Barros de Andrade e Sousa, L. Straßer, L. Heumos, I. I. Mekki, K. T. Mahbubani, A. Sountoulidis, T. Balassa, F. Kovacs, P. Horvath, M. Piraud, A. Ertürk, C. Samakovlis, F. J. Theis, bioRxiv, 2022.
  • Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings, F. Kofler, I. Ezhov, L. Fidon, I. Horvath, E. de la Rosa, J. LaMaster, H. Li, T. Finck, S. Shit, J. Paetzold, S. Bakas, M. Piraud, J. Kirschke, T. Vercauteren, C. Zimmer, B. Wiestler, B. Menze, arXiv, 2022.
  • Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations, S. Shit, I. Ezhov, L. Mächler, J. Lipkova, J. C. Paetzold, F. Kofler, M. Piraud, B. H. Menze, arXiv, 2021.

    • Published papers

  • MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery, T. Siebenmorgen, F. Menezes, S. Benassou, E. Merdivan, S. Kesselheim, M. Piraud, F. J. Theis, M. Sattler, G. M. Popowicz, Nature Computational Science, vol 4, 2024.
  • Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy, J A Buchner, et al., Neuro-oncology, noae098, 2024.
  • Robust Deep Learning based shrimp counting in an industrial farm setting, Bukas C, Albrecht F, Ur-Rehman MS, Popek D, Patalan M, Pawłowski J, Wecker B, Landsch K, Golan T, Kowalczyk T, Piraud M, S S W Ende, Journal of Cleaner Production, vol 468, 2024.
  • Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning, Y. Yu, S. Gawlitt, L. Barros de Andrade e Sousa, E. Merdivan, M. Piraud, C. Beisel, Lars Barquist, Genome Biology, vol 25, 2024.
  • Virtual reality-empowered deep-learning analysis of brain cells, Kaltenecker D, et al., Nature Methods, vol 21, 2024.
  • Reporting electricity consumption is essential for sustainable AI, C. Debus, M. Piraud, A. Streit, F. Theis, M. Götz, Nature Machine Intelligence, vol 5, 2023.
  • Providing AI expertise as an infrastructure in academia, M. Piraud, A. Camero, M. Götz, S. Kesselheim, P. Steinbach, T. Weigel, Patterns, vol 4, 2023.
  • Formate promotes invasion and metastasis in reliance on lipid metabolism, Delbrouck C, et al., Cell Reports, vol 42, 2023.
  • Introduction to AI and its medical applications: Crash Course for an audience with diverse scientific backgrounds, D. Cea, H. Hoffmann, M. Piraud, The Third Teaching Machine Learning and Artificial Intelligence Workshop. PMLR, 2023.
  • Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient, F. Kofler, et al., MELBA Journal, vol 2, 2023.
  • Approaching Peak Ground Truth, F. Kofler, J. Wahle, I. Ezhov, S. Wagner, R. Al-Maskari, E. Gryska, M. Todorov, C. Bukas, F. Meissen, T. Peng, A. Ertürk, D. Rückert, R. Heckemann, J. Kirschke, C. Zimmer, B. Wiestler, B. Menze, M. Piraud, 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023.
  • Echo2Pheno: a deep-learning application to uncover echocardiographic phenotypes in conscious mice, C. Bukas, I. Galter, P. da Silva-Buttkus, H. Fuchs, H. Maier, V. Gailus-Durner, C. L. Müller, M. Hrabĕ; de Angelis, M. Piraud, N. Spielmann, Mammalian Genome, vol 34, 2023.
  • blob loss: instance imbalance aware loss functions for semantic segmentation, F. Kofler, S. Shit, I. Ezhov, L. Fidon, I. Horvath, R. Al-Maskari, H. Li, H. Bhatia, T. Loehr, M. Piraud, A. Ertürk, J. Kirschke, J. Peeken, T. Vercauteren, C. Zimmer, B. Wiestler, B. Menze, International Conference on Information Processing in Medical Imaging (IPMI). Cham: Springer Nature Switzerland, 2023.
  • Genetically encoded barcodes for correlative volume electron microscopy, F. Sigmund, et al., Nature Biotechnology, 2023.
  • Whole-body cellular mapping in mouse using standard IgG antibodies, H. Mai, et al., Nature Biotechnology, 2023.
  • Identifying core MRI sequences for reliable automatic brain metastasis segmentation, J. A. Buchner, et al., Radiotherapy and Oncology, vol 188, 2023.
  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, S. Bakas, et al., Medical Image Analysis, vol 84, 2023.
  • The Liver Tumor Segmentation Benchmark (LiTS), P. Bilic, et al., Medical Image Analysis , 2023.
  • Formate promotes invasion and metastasis in reliance on lipid metabolism, C. Delbrouck, et al., Cell Reports, vol 42, 2023.
  • A hybrid radiomics approach to modeling progression-free survival in head and neck cancers, S. Starke, D. Thalmeier, P. Steinbach, M. Piraud, 3D Head and Neck Tumor Segmentation in PET/CT Challenge. Cham: Springer International Publishing, 2021.
  • Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning, C. Bukas, B. Jian, L. F. Rodriguez Venegas, F. De Benetti, S. Ruehling, A. Sekuboyina, J. Gempt, J. S. Kirschke, M. Piraud, J. Oberreuter, N. Navab, T. Wendler, Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, Proceedings, Springer International Publishing, 2021.
  • DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes, G. Tetteh, V. Efremov, N. D. Forkert, M. Schneider, J. Kirschke, B. Weber, C. Zimmer, M. Piraud, B. H. Menze, Frontiers in Neuroscience, 2020.
  • Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns, M. Möller, M. Kohl, S. Braunewell, F. Kofler, B. Wiestler, J. S. Kirschke, B. H. Menze, M. Piraud, Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Held in Conjunction with MICCAI 2020, Proceedings, Springer International Publishing, 2020.
  • Machine learning analysis of whole mouse brain vasculature, M. I. Todorov, J. C. Paetzold, O. Schoppe, G. Tetteh, S. Shit, V. Efremov, K. Völgyi, M. Düring, M. Dichgans, M. Piraud, B. Menze, A. Ertürk, Nature Methods, 2020.
  • Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee, S. Shit, A. Ravi, I. Ezhov, J. Lipkova, M. Piraud, B. Menze, NeurIPS: Conference on Neural Information Processing Systems, Machine Learning with Guarantees Workshop, 2019.
  • Towards Quantitative Imaging Biomarkers of Tumor Dissemination: a Multi-scale Parametric Modeling of Multiple Myeloma, M. Piraud, M. Wennmann, L. Kintzelé, J. Hillengass, U. Keller, G. Langs, M.-A. Weber, B. H. Menze, Medical Image Analysis, vol 57, 2019.
  • Long Short Term Memory Recurrent Neural Network for MR Fingerprinting parameter estimation, N. Andriamanga, C. M. Pirkl, A. Sekuboyina, G. Buonincontri, M. I. Menzel, P. A. Gómez, M. Piraud , B. H. Menze, ESMRMB 2019, 36th Annual Scientific Meeting, 2019.
  • Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma, R. Licandro, J. Hofmanninger, M. Perkonigg, S. Röhrich, M.-A. Weber, M. Wennmann, L. Kintzele, M. Piraud, B. Menze, G. Langs, MIDL: The Medical Imaging with Deep Learning conference, 2019.
  • Knowledge-aided Convolutional Neural Networks for Small Organ Segmentation, Y. Zhao, H. Li, S. Wan, A. Sekuboyina, X. Hu, G. Tetteh, M. Piraud, B. H. Menze, IEEE J Biomed Health Inform, 2019.
  • Multi-level Activation for Segmentation of Hierarchically-nested Classes, M. Piraud, A. Sekuboyina and B. H. Menze, ECCV 2018 Workshops. Lecture Notes in Computer Science, vol 11134, 2019.
  • Hierarchal multi-class segmentation of glioma using networks with multi-level activation function, X. Hu, H. Li, Y. Zhao, C. Dong, B. H. Menze, and M. Piraud, BrainLes 2018. Lecture Notes in Computer Science, vol 11384, 2019.
  • Volumetry based biomarker speed of growth: Quantifying the change of total tumor volume in whole-body magnetic resonance imaging over time improves risk stratification of smoldering multiple myeloma patients, M. Wennmann, L. Kintzelé, M. Piraud, B. H. Menze, T. Hielscher, J. Hofmanninger, B. Wagner, H.-U. Kauczor, M. Merz, J. Hillengass, G. Langs and M.-A. Weber, Oncotarget, 9 25254, 2018.
  • Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods, L. Xu, G. Tetteh, J. Lipkova, Y. Zhao, H. Li, P. Christ, M. Piraud, A. Buck, K. Shi and B. Menze, Contrast Media & Molecular Imaging 2018, 2391925, 2018.
  • W-Net for Whole-Body Bone Lesion Detection of Multiple Myeloma Patients on 68Ga-Pentixafor PET/CT Imaging, L. Xu, G. Tetteh, M. Mustafa, J. Lipkova, Y. Zhao, M. Bieth, P. Christ, M. Piraud, B. Menze and K. Shi, CMMI/RAMBO/SWITCH 2017, LNCS 10555, pp. 23-30, 2017.
  • Precursor of Laughlin state of hard core bosons on a two leg ladder, A. Petrescu, M. Piraud, G. Roux, I. P. McCulloch and K. Le Hur, Phys. Rev. B, 96 014524, 2017.
  • Symmetry-broken states in a system of interacting bosons on a two-leg ladder with a uniform Abelian gauge field, S. Greschner, M. Piraud, F. Heidrich-Meisner, I. P. McCulloch, U. Schollwöck, and T. Vekua, Phys. Rev. A, 94 063628, 2016.
  • Spontaneous Increase of Magnetic Flux and Chiral-Current Reversal in Bosonic Ladders: Swimming against the Tide, S. Greschner, M. Piraud, F. Heidrich-Meisner, I. P. McCulloch, U. Schollwöck, and T. Vekua, Phys. Rev. Lett., 115 190402, 2015.
  • Strongly interacting bosons on a three-leg ladder in the presence of a homogeneous flux, F. Kolley, M. Piraud, I. P. McCulloch, U. Schollwöck, and F. Heidrich-Meisner, New J. Phys., 17 092001, 2015.
  • Vortex and Meissner phases of strongly-interacting bosons on a two-leg ladder, M. Piraud, F. Heidrich-Meisner, I. P. McCulloch, S. Greschner, T. Vekua, and U. Schollwöck, Phys. Rev. B, 91 140406(R), 2015.
  • Anderson Localization of Matter Waves in 3D Anisotropic Disordered Potentials, M. Piraud, L. Sanchez-Palencia and B. Van Tiggelen, Phys. Rev. A, 90 063639, 2014.
  • Quantum magnetism of bosons with synthetic gauge fields in one-dimensional optical lattices: A density-matrix renormalization-group study, M. Piraud, Z. Cai, I. P. McCulloch, and U. Schollwöck, Phys. Rev. A, 89 063618, 2014.
  • Effects of Interactions and Temperature in Disordered Ultra-Cold Bose Gases, C. Adolphs, J. Towers, M. Piraud, K. V. Krutitsky and D. A. W. Hutchinson, J. Mod. Phys., 5 661-672, 2014.
  • Quantum transport of atomic matterwaves in anisotropic 2D and 3D disorder, M. Piraud, L. Pezzé and L. Sanchez-Palencia, New J. Phys., 15 075007, 2013.
  • Tailoring Anderson localization by disorder correlations in 1D speckle potentials, M. Piraud and L. Sanchez-Palencia, Eur. Phys. J. Special topics, 217 91, 2013.
  • Matter wave transport and Anderson localization in anisotropic three-dimensional disorder, M. Piraud, L. Pezzé and L. Sanchez-Palencia, Europhys. Lett., 99 50003, 2012.
  • Anderson localization of matter waves in tailored disordered potentials, M. Piraud, A. Aspect and L. Sanchez-Palencia, Phys. Rev. A, 85 063611, 2012.
  • Three-dimensional localization of ultracold atoms in an optical disordered potential, F. Jendrzejewski, A. Bernard, K. Mueller, P. Cheinet, V. Josse, M. Piraud, L. Pezzé, L. Sanchez-Palencia, A. Aspect and P. Bouyer, Nature Phys., 8 398 , 2012.
  • Localization of a matter wave packet in a disordered potential, M. Piraud, P. Lugan, P. Bouyer, A. Aspect and L. Sanchez-Palencia, Phys. Rev. A, 83 031603(R), 2011.