| Time series classification in reservoir-and model-space W Aswolinskiy, RF Reinhart, J Steil Neural Processing Letters 48 (2), 789-809, 2018 | 25 | 2018 |
| Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations N Marini, S Marchesin, S Otálora, M Wodzinski, A Caputo, ... NPJ digital medicine 5 (1), 102, 2022 | 24 | 2022 |
| Time series classification in reservoir-and model-space: a comparison W Aswolinskiy, RF Reinhart, J Steil Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop …, 2016 | 24 | 2016 |
| RM-SORN: a reward-modulated self-organizing recurrent neural network W Aswolinskiy, G Pipa Frontiers in computational neuroscience 9, 36, 2015 | 23 | 2015 |
| Impact of Regularization on the Model Space for Time Series Classification W Aswolinskiy, RF Reinhart, J Steil Workshop New Challenges in Neural Computation, 2015 | 16 | 2015 |
| Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images W Aswolinskiy, D Tellez, G Raya, L van der Woude, M Looijen-Salamon, ... Medical Imaging 2021: Digital Pathology 11603, 1160304, 2021 | 9 | 2021 |
| Unsupervised transfer learning for time series via self-predictive modelling-first results W Aswolinskiy, B Hammer Proceedings of the Workshop on New Challenges in Neural Computation (NC2) 3, 2017 | 9 | 2017 |
| Modelling of parameterized processes via regression in the model space W Aswolinskiy, F Reinhart, JJ Steil Proceedings of 24th European Symposium on Artificial Neural Networks, 2016 | 9 | 2016 |
| Modelling of parametrized processes via regression in the model space of neural networks W Aswolinskiy, RF Reinhart, JJ Steil Neurocomputing 268, 55-63, 2017 | 7 | 2017 |
| Gigapixel end-to-end training using streaming and attention S Dooper, H Pinckaers, W Aswolinskiy, K Hebeda, S Jarkman, ... Medical Image Analysis, 102881, 2023 | 4 | 2023 |
| Maschinelles lernen in technischen systemen F Reinhart, K Neumann, W Aswolinskiy, J Steil, B Hammer Steigerung der Intelligenz mechatronischer Systeme, 73-118, 2018 | 4 | 2018 |
| Learning in the model space of neural networks W Aswolinskiy | 2 | 2018 |
| Parameterized pattern generation via regression in the model space of echo state networks W Aswolinskiy, JJ Steil Proceedings of the Workshop on New Challenges in Neural Computation, 2016 | 1 | 2016 |
| PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning W Aswolinskiy, E Munari, HM Horlings, L Mulder, G Bogina, J Sanders, ... Breast Cancer Research 25 (1), 142, 2023 | | 2023 |
| Caption generation from histopathology whole-slide images using pre-trained transformers BC Guevara, N Marini, S Marchesin, W Aswolinskiy, RJ Schlimbach, ... Medical Imaging with Deep Learning, short paper track, 2023 | | 2023 |
| Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies W Aswolinskiy, E Munari, HM Horlings, L Mulder, G Bogina, J Sanders, ... medRxiv, 2022.11. 11.22282205, 2022 | | 2022 |
| Basal cell carcinoma detection using weakly supervised deep learning methods and rule-based labels D Geijs, S Dooper, W Aswolinskiy, L van Eekelen, A Amir, G Litjens | | 2022 |
| Potential of an AI-based digital biomarker to predict neoadjuvant chemotherapy response from preoperative biopsies of Luminal-B breast cancer W Aswolinskiy, H Horlings, L Mulder, J Van der Laak, J Wesseling, E Lips, ... VIRCHOWS ARCHIV 479, S179-S179, 2021 | | 2021 |
Gordon PipaProfessor for neuroinformatics - Osnabrueck UniversityVerified email at uos.de
