| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 391 | 2019 |
| Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning F Klauschen, KR Müller, A Binder, M Bockmayr, M Hägele, P Seegerer, ... Seminars in cancer biology 52, 151-157, 2018 | 153 | 2018 |
| Resolving challenges in deep learning-based analyses of histopathological images using explanation methods M Hägele, P Seegerer, S Lapuschkin, M Bockmayr, W Samek, ... Scientific Reports 10 (1), 1-12, 2020 | 141 | 2020 |
| Morphological and molecular breast cancer profiling through explainable machine learning A Binder, M Bockmayr, M Hägele, S Wienert, D Heim, K Hellweg, M Ishii, ... Nature Machine Intelligence 3 (4), 355-366, 2021 | 110 | 2021 |
| Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles A Binder, M Bockmayr, M Hägele, S Wienert, D Heim, K Hellweg, ... arXiv preprint arXiv:1805.11178, 2018 | 32 | 2018 |
| How to iNNvestigate neural network’s predictions! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... | 4 | 2018 |
| Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology M Aversa, G Nobis, M Hägele, K Standvoss, M Chirica, R Murray-Smith, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| EEG correlates of visual recognition while overtly tracking a moving object M Ušćumlić, M Hägele, B Blankertz International Workshop on Symbiotic Interaction, 166-171, 2015 | 3 | 2015 |
| 68MO Generalization of a deep learning model for HER2 status predictions on H&E-stained whole slide images derived from 3 neoadjuvant clinical studies M Hägele, KR Müller, C Denkert, A Schneeweiss, BV Sinn, M Untch, ... Annals of Oncology 33, S572-S573, 2022 | 2 | 2022 |
| Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides M Hägele, J Eschrich, L Ruff, M Alber, S Schallenberg, A Guillot, ... arXiv preprint arXiv:2302.01813, 2023 | 1 | 2023 |
| Advancing computer vision algorithms to overcome challenges in computational pathology TM Hägele | 1 | 2022 |
