A Deep Variational Approach to Clustering Survival Data L Manduchi, R Marcinkevičs, MC Massi, T Weikert, A Sauter, V Gotta, ... ICRL 2022, 2022 | 36 | 2022 |
Deep conditional gaussian mixture model for constrained clustering L Manduchi, K Chin-Cheong, H Michel, S Wellmann, J Vogt Advances in Neural Information Processing Systems 34, 11303-11314, 2021 | 24 | 2021 |
DPSOM: Deep probabilistic clustering with self-organizing maps L Manduchi, M Hüser, J Vogt, G Rätsch, V Fortuin NeurIPS 2019 workshop on Machine Learning for Health, 2019 | 21 | 2019 |
T-dpsom: An interpretable clustering method for unsupervised learning of patient health states L Manduchi, M Hüser, M Faltys, J Vogt, G Rätsch, V Fortuin Proceedings of the Conference on Health, Inference, and Learning, 236-245, 2021 | 20 | 2021 |
DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds F Laumer, G Fringeli, A Dubatovka, L Manduchi, JM Buhmann Machine Learning for Health, 194-212, 2020 | 13 | 2020 |
Weakly supervised inference of personalized heart meshes based on echocardiography videos F Laumer, M Amrani, L Manduchi, A Beuret, L Rubi, A Dubatovka, ... Medical image analysis 83, 102653, 2023 | 9 | 2023 |
Learning Group Importance using the Differentiable Hypergeometric Distribution TM Sutter, L Manduchi, A Ryser, JE Vogt ICLR 2023, 2023 | 6 | 2023 |
Interpretable prediction of pulmonary hypertension in newborns using echocardiograms H Ragnarsdottir, L Manduchi, H Michel, F Laumer, S Wellmann, E Ozkan, ... DAGM German Conference on Pattern Recognition, 529-542, 2022 | 5 | 2022 |
On the challenges and opportunities in generative ai L Manduchi, K Pandey, R Bamler, R Cotterell, S Däubener, S Fellenz, ... arXiv preprint arXiv:2403.00025, 2024 | 4 | 2024 |
Predicting second breast cancer among women with primary breast cancer using machine learning algorithms, a population‐based observational study ME Syleouni, N Karavasiloglou, L Manduchi, M Wanner, D Korol, L Ortelli, ... International journal of cancer 153 (5), 932-941, 2023 | 2 | 2023 |
Tree Variational Autoencoders L Manduchi, M Vandenhirtz, A Ryser, J Vogt 37th Conference on Neural Information Processing Systems, 2023 | 2 | 2023 |
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models A Ryser, L Manduchi, F Laumer, H Michel, S Wellmann, JE Vogt Machine Learning for Healthcare Conference, 425-458, 2022 | 2* | 2022 |
Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms H Ragnarsdottir, E Ozkan, H Michel, K Chin-Cheong, L Manduchi, ... International Journal of Computer Vision, 1-18, 2024 | 1 | 2024 |
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data M Vandenhirtz, F Barkmann, L Manduchi, JE Vogt, V Boeva arXiv preprint arXiv:2406.19300, 2024 | | 2024 |
Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders E Palumbo, L Manduchi, S Laguna, D Chopard, JE Vogt The Twelfth International Conference on Learning Representations, 2024 | | 2024 |
Dagstuhl Reports, Vol. 13, Issue 2 ISSN 2192-5283 N Megow, BJ Moseley, D Shmoys, O Svensson, S Vassilvitskii, J Schlöter, ... | | 2023 |
Ein Deep-Learning-Ansatz zur automatisierten Detektion von pulmonaler Hypertension beim Neugeborenen anhand von 2D-Echokardiographie-Videos H Michel, H Ragnarsdottir, L Manduchi, F Laumer, E Özkan, J Vogt, ... Zeitschrift für Geburtshilfe und Neonatologie 227 (03), FV071, 2023 | | 2023 |
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss M Vandenhirtz, L Manduchi, R Marcinkevičs, JE Vogt arXiv preprint arXiv:2305.19671, 2023 | | 2023 |
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072) V Fortuin, Y Li, K Murphy, S Mandt, L Manduchi Dagstuhl Reports 13 (2), 2023 | | 2023 |
Predicting second breast cancers among women diagnosed with primary breast cancer using patient-level data and machine learning algorithms ME Syleouni, N Karavasiloglou, L Manduchi, M Wanner, D Korol, ... Cancer Research 82 (12_Supplement), 2252-2252, 2022 | | 2022 |