2026S

• Medical Image Processing and Machine Learning / 193.215 / VU

2024

• FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection / Gutwein, S., Kampel, M., Taschner-Mandl, S., & Licandro, R. (2024). FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection. In C. H. Sudre & R. Mehta (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 23–33). https://doi.org/10.1007/978-3-031-73158-7_3

2022

• Dear Reviewers: Responses to Common Reviewer Critiques about Infant Neuroimaging Studies / Korom, M., Camacho, M. C., Filippi, C. A., Licandro, R., Moore, L. A., Dufford, A., Zöllei, L., Graham, A. M., Spann, M., Howell, B., Shultz, S., Scheinost, D., & FIT´NG. (2022). Dear Reviewers: Responses to Common Reviewer Critiques about Infant Neuroimaging Studies. Developmental Cognitive Neuroscience, 53, Article 101055. https://doi.org/10.1016/j.dcn.2021.101055
• Modellierung und Simulation – Erstellen Sie Ihre eigenen Modelle / Zauner, G., & Weidinger, W. (2022). Modellierung und Simulation – Erstellen Sie Ihre eigenen Modelle. In S. Papp, W. Weidinger, K. Munro, B. Ortner, A. Cadonna, G. Langs, R. Licandro, M. Meir-Huber, D. Nikolić, Z. Toth, B. Vesela, R. Wazir, & G. Zauner (Eds.), Handbuch Data Science und KI: Mit Machine Learning und Datenanalyse Wert aus Daten generieren (pp. 379–421). https://doi.org/10.3139/9783446472457.012

2021

• SHREC 2021: Retrieval of cultural heritage objects / Sipiran, I., Lazo, P., Lopez, C., Jimenez, M., Bagewadi, N., Bustos, B., Dao, H., Gangisetty, S., Hanik, M., Ho-Thi, N.-P., Holenderski, M., Jarnikov, D., Labrada, A., Lengauer, S., Licandro, R., Nguyen, D.-H., Nguyen-Ho, T.-L., Perez Rey, L. A., Pham, B.-D., & Vu-Le, T.-A. (2021). SHREC 2021: Retrieval of cultural heritage objects. COMPUTERS & GRAPHICS-UK, 100, 1–20. https://doi.org/10.1016/j.cag.2021.07.010
• Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis : 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / Sudre, C. H., Licandro, R., Baumgartner, C., Melbourne, A., Dalca, A., Hutter, J., Tanno, R., Turk, E. A., Van Leemput, K., Torrents-Barrena, J., Wells, W. M., & Macgowan, C. (Eds.). (2021). Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis : 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. In Lecture Notes in Computer Science (Vol. 12959). Springer. https://doi.org/10.1007/978-3-030-87735-4
• Fetal Brain Tissue Annotation and Segmentation Challenge / Payette, K., Dumast, P. de, Jakab, A., Cuadra, M. B., Vasung, L., Licandro, R., Menze, B., & Li, H. (2021). Fetal Brain Tissue Annotation and Segmentation Challenge. In 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021) (p. 15). Zenodo. https://doi.org/10.5281/zenodo.4573144
• Reconstruction of Optical Coherence Tomography Images Retrieved from Discontinuous Spectral Data using Conditional Generative Adversarial Network / Lichtenegger, A., Salas, M., Sing, A., Duelk, M., Licandro, R., Gesperger, J., Baumann, B., Drexler, W., & Leitgeb, R. A. (2021). Reconstruction of Optical Coherence Tomography Images Retrieved from Discontinuous Spectral Data using Conditional Generative Adversarial Network. Biomedical Optics Express, 12(11), 6780. https://doi.org/10.1364/boe.435124
• Spatio Temporal Risk Prediction of Focal Bone Lesion Evolution in Multiple Myeloma / Licandro, R., Perkonigg, M., Röhrich, S., Weber, M.-A., Wennmann, M., Kintzele, L., Piraud, M., Menze, B., & Langs, G. (2021). Spatio Temporal Risk Prediction of Focal Bone Lesion Evolution in Multiple Myeloma. European Congress of Radiology (ECR 2021), Wien, Austria. http://hdl.handle.net/20.500.12708/87262
• Spatio temporal modelling of dynamic developmental patterns / Licandro, R. (2021). Spatio temporal modelling of dynamic developmental patterns [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.39603
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2020

• Evolution Risk Prediction of Bone Lesions in Multiple Myeloma / Licandro, R., Hofmanninger, J., Perkonigg, M., Röhrich, S., Weber, M.-A., Wennmann, M., Kintzele, L., Piraud, M., Menze, B., & Langs, G. (2020). Evolution Risk Prediction of Bone Lesions in Multiple Myeloma. European Congress of Radiology 2020, Wien, Austria. http://hdl.handle.net/20.500.12708/87090
• Maschinelles Lernen in der Radiologie: Begriffsbestimmung vom Einzelzeitpunkt bis zur Trajektorie / Langs, G., Attenberger, U., Licandro, R., Hofmanninger, J., Perkonigg, M., Zusag, M., Röhrich, S., Sobotka, D., & Prosch, H. (2020). Maschinelles Lernen in der Radiologie: Begriffsbestimmung vom Einzelzeitpunkt bis zur Trajektorie. Der Radiologe, 60(1), 6–14. https://doi.org/10.1007/s00117-019-00624-x
• Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis / Hu, Y., Licandro, R., Noble, A., Hutter, J., Aylward, S., Melbourne, A., Turk, E. A., & Torrents Barrena, J. (Eds.). (2020). Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. Springer International Publishing. https://doi.org/10.1007/978-3-030-60334-2
• Evaluation of confound regression strategies for denoising in-utero resting-state functional MRI / Taymourtash, A., Schwartz, E., Nenning, K. H., Licandro, R., Sobotka, D., Diogo, M., Prayer, D., Kasprian, G., & Langs, G. (2020). Evaluation of confound regression strategies for denoising in-utero resting-state functional MRI. Organization for Human Brain Mapping (OHMB) 2020, Montreal, Canada. http://hdl.handle.net/20.500.12708/87092
• Machine Learning in Medical Imaging / Licandro, R. (2020). Machine Learning in Medical Imaging. Summer School on Image Processing (SSIP) 2020, Szeged, Hungary. http://hdl.handle.net/20.500.12708/87093
• Functional thalamocortical connectivity development revealed by in-utero resting state fMRI / Taymourtash, A., Schwartz, E., Nenning, K. H., Licandro, R., Sobotka, D., Diogo, M., Golland, P., Grant, E., Kasprian, G., Prayer, D., & Langs, G. (2020). Functional thalamocortical connectivity development revealed by in-utero resting state fMRI. In Utero-MRI Workshop, Oxford, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/87091

2019

• Preface PIPPI 2019 / Hutter, J., Licandro, R., Robinson, E., Christiaens, D., Turk, E. A., & Melbourne, A. (2019). Preface PIPPI 2019. In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis : First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (p. 10). Springer. http://hdl.handle.net/20.500.12708/57974
• Whole-body MRI based lesion prediction in multiple myeloma / Licandro, R., Hofmanninger, J., Weber, M.-A., Menze, B., & Langs, G. (2019). Whole-body MRI based lesion prediction in multiple myeloma. Insights into Imaging, 10. http://hdl.handle.net/20.500.12708/57978
• GMM Interpolation for Blood Cell Cluster Alignment in Childhood Leukaemia / Licandro, R., Miloserdov, K., Reiter, M., & Kampel, M. (2019). GMM Interpolation for Blood Cell Cluster Alignment in Childhood Leukaemia. In A. Pichler, P. M. Roth, R. Sablatnig, G. Stübl, & M. Vincze (Eds.), Proceedings of the ARW & OAGM Workshop 2019 (pp. 179–182). Verlag der Technischen Universität Graz. https://doi.org/10.3217/978-3-85125-663-5-39
• Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI / Sobotka, D., Licandro, R., Ebner, M., Ourselin, S., Kasprian, G., Prayer, D., & Langs, G. (2019). Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI. In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis (pp. 123–132). Springer. https://doi.org/10.1007/978-3-030-32875-7_14
• Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma / Licandro, R., Hofmanninger, J., Perkonigg, M., Röhrich, S., Weber, M.-A., Wennmann, M., Kintzele, L., Piraud, M., Menze, B., & Langs, G. (2019). Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma. In Medical Imaging with Deep Learning: MIDL 2019. International Conference on Medical Imaging with Deep Learning (MIDL), London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/57975

2018

• Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / Melbourne, A., Licandro, R., DiFranco, M., Rota, P., Gau, M., Kampel, M., Aughwane, R., Moeskops, T., Schwartz, E., Robinson, E., & Makropoulos, A. (Eds.). (2018). Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Springer. https://doi.org/10.1007/978-3-030-00807-9
• Predicting Future Bone Infiltration Patterns in Multiple Myeloma / Licandro, R., Hofmanninger, J., Weber, M.-A., Menze, B., & Langs, G. (2018). Predicting Future Bone Infiltration Patterns in Multiple Myeloma. In W. Bai, G. Sanroma, G. Wu, B. C. Munsell, Y. Zhan, & P. Coupé (Eds.), Patch-Based Techniques in Medical Imaging. Patch-MI 2018 (pp. 76–84). Springer. https://doi.org/10.1007/978-3-030-00500-9_9
• WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia / Licandro, R., Schlegl, T., Reiter, M., Diem, M., Dworzak, M., Schumich, A., Langs, G., & Kampel, M. (2018). WGAN Latent Space Embeddings for Blast Identification in Childhood Acute Myeloid Leukaemia. In 2018 24th International Conference on Pattern Recognition (ICPR). 24th International Conference on Pattern Recognition (ICPR) 2018, Peking, China. IEEE. https://doi.org/10.1109/icpr.2018.8546177
• Monitoring Acute Lymphoblastic Leukaemia Therapy with Stacked Denoising Autoencoders / Scheithe, J., Licandro, R., Rota, P., Reiter, M., Diem, M., & Kampel, M. (2018). Monitoring Acute Lymphoblastic Leukaemia Therapy with Stacked Denoising Autoencoders. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images. International Conference on Clinical and Medical Image Analysis (ICCMIA18), Tamilnadu, India. Springer. http://hdl.handle.net/20.500.12708/57569
• Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia / Licandro, R., Reiter, M., Diem, M., Dworzak, M., Schumich, A., & Kampel, M. (2018). Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Funchal, Portugal. ScitePress. https://doi.org/10.5220/0006595804010408
• Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders / Licandro, R., Hofmanninger, J., Menze, B., Weber, M.-A., & Langs, G. (2018). Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders. 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Funchal, Portugal. http://hdl.handle.net/20.500.12708/86780
• Dimensionality reduction for analysis of functional connectivity in the developing human brain / Frauenstein, L., Nenning, K. H., Schwartz, E., Licandro, R., Bühler, K., & Langs, G. (2018). Dimensionality reduction for analysis of functional connectivity in the developing human brain. Forum of Neuroscience (11th FENS 2018), Berlin, Germany. http://hdl.handle.net/20.500.12708/86781
• Early Predictors of Bone Infiltration in Multiple Myeloma Patients from T2 weighted MRI images / Licandro, R., Hofmanninger, J., Weber, M.-A., Menze, B., & Langs, G. (2018). Early Predictors of Bone Infiltration in Multiple Myeloma Patients from T2 weighted MRI images. In Proceedings of the OAGM Workshop 2018 Medical Image Analysis (pp. 9–12). Verlag der Technischen Universität Graz. http://hdl.handle.net/20.500.12708/56022

2017

• Assessing Reorganisation of Functional Connectivity in the Infant Brain / Licandro, R., Nenning, K. H., Schwartz, E., Kollndorfer, K., Bartha-Doering, L., Hesheng, L., & Langs, G. (2017). Assessing Reorganisation of Functional Connectivity in the Infant Brain. In Fetal, Infant and Ophthalmic Medical Image Analysis (pp. 14–24). Springer. https://doi.org/10.1007/978-3-319-67561-9_2
• Spatio Temporal Assessment of Developing Patterns: from Fetal MR Imaging to Flowcytometry / Licandro, R. (2017). Spatio Temporal Assessment of Developing Patterns: from Fetal MR Imaging to Flowcytometry. Health Tech Lunch 2017, Sierre, Switzerland. http://hdl.handle.net/20.500.12708/86618
• Spatio Temporal Modelling of Dynamic Developmental Patterns / Licandro, R. (2017). Spatio Temporal Modelling of Dynamic Developmental Patterns. Medical Image Computing Summer School (MedICSS), London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/86617

2016

• Longitudinal Atlas Learning for Fetal Brain Tissue Labeling using Geodesic Regression / Licandro, R., Langs, G., Kasprian, G., Sablatnig, R., Prayer, D., & Schwartz, E. (2016). Longitudinal Atlas Learning for Fetal Brain Tissue Labeling using Geodesic Regression. Woman in Computer Vision (WiCV) Workshop at the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, United States of America (the). http://hdl.handle.net/20.500.12708/86367
• A Longitudinal Diffeomorphic Atlas-Based Tissue Labeling Framework for Fetal Brains using Geodesic Regression / Licandro, R., Langs, G., Kasprian, G., Sablatnig, R., Prayer, D., & Schwartz, E. (2016). A Longitudinal Diffeomorphic Atlas-Based Tissue Labeling Framework for Fetal Brains using Geodesic Regression. In Proceedings of the 21st Computer Vision Winter Workshop. 21st Computer Vision Winter Workshop (CVWW2016), Rimske Toplice, Slovenia. Slovenian Pattern Recognition Society. http://hdl.handle.net/20.500.12708/56654
• Flow Cytometry based automatic MRD assessment in Acute Lymphoblastic Leukaemia: Longitudinal evaluation of time-specific cell population models / Licandro, R., Rota, P., Reiter, M., & Kampel, M. (2016). Flow Cytometry based automatic MRD assessment in Acute Lymphoblastic Leukaemia: Longitudinal evaluation of time-specific cell population models. In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI). 14th International Workshop on Content-based Multimedia Indexing, Bukarest, Romania. IEEE. https://doi.org/10.1109/cbmi.2016.7500274
• Changing Functional Connectivity in the Child's Developing Brain Affected by Ischaemic Stroke / Licandro, R., Nenning, K. H., Schwartz, E., Kollndorfer, K., Bartha-Doering, L., & Langs, G. (2016). Changing Functional Connectivity in the Child’s Developing Brain Affected by Ischaemic Stroke. In MICCAI satellite workshop on Perinatal, Preterm and Paediatric Imaging Proceedings of PIPPI 2016 (p. 10). http://hdl.handle.net/20.500.12708/56649
• Automatic Detection of Leukaemic Cells in Flow Cytometric Data for Minimal Residual Disease Assessment / Licandro, R., Rota, P., Reiter, M., Kleber, F., Diem, M., & Kampel, M. (2016). Automatic Detection of Leukaemic Cells in Flow Cytometric Data for Minimal Residual Disease Assessment. EuroScience Open forum (ESOF) - Marie Sklodowska-Curie Actions Satellite Event `Research and Society’, Manchester, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/86365
• Spatio Temporal Modelling of Dynamic Developmental Pattern / Licandro, R., Langs, G., & Kampel, M. (2016). Spatio Temporal Modelling of Dynamic Developmental Pattern. International Computer Vision Summer School (ICVSS), Sizilien, Italy. http://hdl.handle.net/20.500.12708/86366
• Automatic MRD Assessment in Flow Cytometry of Different Leukaemia Types in Children: An Overview / Licandro, R. (2016). Automatic MRD Assessment in Flow Cytometry of Different Leukaemia Types in Children: An Overview. Workshop on the ICT Contribution to the Development of Clinical Applications, Wien, Austria. http://hdl.handle.net/20.500.12708/86369
• AutoFLOW: a novel heuristic method to automatically detect leukaemic cells in flow cytometric data / Licandro, R., Rota, P., Reiter, M., & Kampel, M. (2016). AutoFLOW: a novel heuristic method to automatically detect leukaemic cells in flow cytometric data. 3rd Austrian Biomarker Symposium 2016 on early diagnostics, Wien, Austria. http://hdl.handle.net/20.500.12708/86368
• Longitudinal influence assessment of paediatric stroke events on resting state networks / Licandro, R., Nenning, K. H., Kollndorfer, K., Bartha-Doering, L., & Langs, G. (2016). Longitudinal influence assessment of paediatric stroke events on resting state networks. 5th Biennial Conference on Resting State Brain Connectivity, Wien, Austria. http://hdl.handle.net/20.500.12708/86363

2015

• Longitudinal diffeomorphic fetal brain atlas learning for tissue labeling using geodesic regression and graph cuts / Licandro, R. (2015). Longitudinal diffeomorphic fetal brain atlas learning for tissue labeling using geodesic regression and graph cuts [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2015.21890
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2014

• Longitudinal Diffeomorphic Fetal Brain Atlas Learning For Tissue Labeling Using Geodesic Regression And Graph Cuts / Licandro, R., Schwartz, E., Langs, G., & Sablatnig, R. (2014). Longitudinal Diffeomorphic Fetal Brain Atlas Learning For Tissue Labeling Using Geodesic Regression And Graph Cuts. Medical Imaging Summer School 2014, Favignana, Italy. http://hdl.handle.net/20.500.12708/85904

• Classification of tumor cells in childhood cancer using automated microscopy and deep learning / Temme, J. (2023). Classification of tumor cells in childhood cancer using automated microscopy and deep learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.93784
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• Modelling the developing connectome : Graph representation learning with variational autoencoders / Miloserdov, K. (2022). Modelling the developing connectome : Graph representation learning with variational autoencoders [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.91901
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