| Leveraging uncertainty information from deep neural networks for disease detection C Leibig, V Allken, MS Ayhan, P Berens, S Wahl Scientific reports 7 (1), 1-14, 2017 | 575 | 2017 |
| Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis C Leibig, M Brehmer, S Bunk, D Byng, K Pinker, L Umutlu The Lancet Digital Health 4 (7), e507-e519, 2022 | 143 | 2022 |
| Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis C Leibig, T Wachtler, G Zeck Journal of neuroscience methods 271, 1-13, 2016 | 47 | 2016 |
| Inflammatory stimulation preserves physiological properties of retinal ganglion cells after optic nerve injury H Stutzki, C Leibig, A Andreadaki, D Fischer, G Zeck Frontiers in cellular neuroscience 8, 38, 2014 | 40 | 2014 |
| AI-based prevention of interval cancers in a national mammography screening program D Byng, B Strauch, L Gnas, C Leibig, O Stephan, S Bunk, G Hecht European Journal of Radiology 152, 110321, 2022 | 23 | 2022 |
| Machine Learning based Predictions of Subjective Refractive Errors of the Human Eye. A Leube, C Leibig, A Ohlendorf, S Wahl HEALTHINF, 199-205, 2019 | 6 | 2019 |
| Nationwide real-world implementation of AI for cancer detection in population-based mammography screening N Eisemann, S Bunk, T Mukama, H Baltus, SA Elsner, T Gomille, G Hecht, ... Nature Medicine, 1-8, 2025 | 5 | 2025 |
| Apparatus for ascertaining predicted subjective refraction data or predicted correction values, and computer program A Ohlendorf, S Wahl, C Leibig, A Leube US Patent App. 16/404,991, 2019 | 4 | 2019 |
| Abstract ot3-18-03: the praim study: a prospective multicenter observational study of an integrated artificial intelligence system with live monitoring D Byng, N Eisemann, D Schüler, S Bunk, C Leibig, M Brehmer, S Elsner, ... Cancer Research 83 (5_Supplement), OT3-18-03-OT3-18-03, 2023 | 3 | 2023 |
| A machine learning approach to determine refractive errors of the eye A Ohlendorf, A Leube, C Leibig, S Wahl Investigative Ophthalmology & Visual Science 58 (8), 1136-1136, 2017 | 3 | 2017 |
| Discriminative Bayesian neural networks know what they do not know C Leibig, S Wahl NIPS Workshop: Deep Learning and Representation Learning, 2016 | 2 | 2016 |
| Resolution Limit of Neurochip Data C Leibig, T Wachtler, G Zeck Front. Comput. Neurosci. Conference Abstract: BC11: Computational …, 2011 | 2 | 2011 |
| Unsupervised neural spike identification for large-scale, high-density micro-electrode arrays C Leibig Universität Tübingen, 2016 | 1 | 2016 |
| Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis ZV Fisches, M Ball, T Mukama, V Štih, NR Payne, SE Hickman, FJ Gilbert, ... The Lancet Digital Health 6 (11), e803-e814, 2024 | | 2024 |
| System and method for identifying breast cancer C Leibig, S Bunk, M Brandstaetter US Patent App. 17/731,229, 2023 | | 2023 |
| Brustkrebsvorsorge: künstliche Intelligenz verbessert Diagnose C Leibig TumorDiagn u Ther 43, 2022 | | 2022 |
| AI-based prevention of interval cancers in a population-based breast cancer program D Byng, B Strauch, L Gnas, C Leibig, O Stephan, S Bunk, G Hecht ‘ONE SIZE DOES NOT FIT ALL’, 213, 2022 | | 2022 |
| Method for optimizing an optical aid by way of automatic subjective visual performance measurement A Leube, C Leibig, A Ohlendorf, S Wahl US Patent 11,143,886, 2021 | | 2021 |
| Activity patterns of degenerating retinal projection neurons mapped with a CMOS multitransistorarray C Leibig Universität Konstanz Konstanz, 2010 | | 2010 |
| Leveraging uncertainty information from deep C Leibig, V Allken, MS Ayhan, P Berens, S Wahl | | |
