| Residual deep convolutional neural network for eeg signal classification in epilepsy D Lu, J Triesch arXiv preprint arXiv:1903.08100, 2019 | 63 | 2019 |
| Human-expert-level brain tumor detection using deep learning with data distillation and augmentation D Lu, N Polomac, I Gacheva, E Hattingen, J Triesch ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 25 | 2021 |
| Staging epileptogenesis with deep neural networks D Lu, S Bauer, V Neubert, LS Costard, F Rosenow, J Triesch Proceedings of the 11th ACM International Conference on Bioinformatics …, 2020 | 8 | 2020 |
| Towards early diagnosis of epilepsy from eeg data D Lu, S Bauer, V Neubert, LS Costard, F Rosenow, J Triesch machine learning for healthcare conference, 80-96, 2020 | 7 | 2020 |
| A deep residual neural network based framework for epileptogenesis detection in a rodent model with single-channel EEG recordings D Lu, S Bauer, V Neubert, LS Costard, F Rosenow, J Triesch 2019 12th International Congress on Image and Signal Processing, BioMedical …, 2019 | 5 | 2019 |
| Residual deep convolutional neural network for eeg signal classification in epilepsy. arXiv 2019 D Lu, J Triesch arXiv preprint arXiv:1903.08100, 0 | 5 | |
| Diagnosing epileptogenesis with deep anomaly detection A Farahat, D Lu, S Bauer, V Neubert, LS Costard, F Rosenow, J Triesch Machine Learning for Healthcare Conference, 325-342, 2022 | 4 | 2022 |
| Energy efficient path planning of autonomous underwater vehicles for environment modeling D Lu, R Cui, P Wang 2014 International Conference on Multisensor Fusion and Information …, 2014 | 4 | 2014 |
| Multiple instance learning for brain tumor detection from magnetic resonance spectroscopy data D Lu, G Kurz, N Polomac, I Gacheva, E Hattingen, J Triesch arXiv preprint arXiv:2112.08845, 2021 | 1 | 2021 |
| Advancing Brain Tumor Detection with Multiple Instance Learning on Magnetic Resonance Spectroscopy Data D Lu, G Kurz, N Polomac, I Gacheva, E Hattingen, J Triesch International Conference on Artificial Neural Networks, 1-12, 2023 | | 2023 |
| Machine learning for healthcare with a focus on the early diagnosis of epilepsy and brain tumor detection D Lu Universitätsbibliothek Johann Christian Senckenberg, 2022 | | 2022 |
| P2. Unsupervised anomaly detection for diagnosing brain disorders from EEG recordings–Results from a rodent epilepsy model A Farahat, D Lu, S Bauer, F Rosenow, J Triesch Clinical Neurophysiology 132 (8), e1-e2, 2021 | | 2021 |
| FV18 Towards epileptogenesis staging with deep neural networks D Lu, S Bauer, V Neubert, LS Costard, F Rosenow, J Triesch Clinical Neurophysiology 131 (4), e231-e232, 2020 | | 2020 |
| Multiple Instance Learning for Brain Tumor Detection from MRS Data D Lu, G Kurz, N Polomac, I Gacheva, E Hattingen, J Triesch | | |
| MULTIPLE INSTANCE-BASED TUMOR DETECTION FROM MAGNETIC RESONANCE SPECTROSCOPY DATA D Lu, G Kurz, N Polomac, I Gacheva, E Hattingen, J Triesch | | |
