| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of Machine Learning Research 20 (93), 1-8, 2019 | 423 | 2019 |
| Pruning by explaining: A novel criterion for deep neural network pruning SK Yeom, P Seegerer, S Lapuschkin, A Binder, S Wiedemann, KR Müller, ... Pattern Recognition 115, 107899, 2021 | 234 | 2021 |
| 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 | 162 | 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), 6423, 2020 | 159 | 2020 |
| Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases P Jurmeister, M Bockmayr, P Seegerer, T Bockmayr, D Treue, ... Science Translational Medicine 11 (509), eaaw8513, 2019 | 118 | 2019 |
| Towards personalized cardiology: multi-scale modeling of the failing heart E Kayvanpour, T Mansi, F Sedaghat-Hamedani, A Amr, D Neumann, ... PLoS One 10 (7), e0134869, 2015 | 98 | 2015 |
| Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals O Zettinig, T Mansi, D Neumann, B Georgescu, S Rapaka, P Seegerer, ... Medical image analysis 18 (8), 1361-1376, 2014 | 61 | 2014 |
| System and method for characterization of electrical properties of the heart from medical images and body surface potentials P Seegerer, T Mansi, MP Jolly, B Georgescu, A Kamen, D Comaniciu, ... US Patent 10,141,077, 2018 | 48 | 2018 |
| DNA methylation-based classification of sinonasal tumors P Jurmeister, S Glöß, R Roller, M Leitheiser, S Schmid, LH Mochmann, ... Nature Communications 13 (1), 7148, 2022 | 39 | 2022 |
| Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation M Leitheiser, D Capper, P Seegerer, A Lehmann, U Schüller, KR Müller, ... The Journal of Pathology 256 (4), 378-387, 2022 | 27 | 2022 |
| Semantic segmentation for cancer detection in digital breast tomosynthesis LM Itu, L Lazar, S Liu, O Pauly, P Seegerer, II Stroia, A Turcea, A Vizitiu, ... US Patent 10,779,785, 2020 | 25 | 2020 |
| Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images P Seegerer, A Binder, R Saitenmacher, M Bockmayr, M Alber, ... Artificial Intelligence and Machine Learning for Digital Pathology: State-of …, 2020 | 16 | 2020 |
| Estimation of regional electrical properties of the heart from 12-lead ECG and images P Seegerer, T Mansi, MP Jolly, D Neumann, B Georgescu, A Kamen, ... International Workshop on Statistical Atlases and Computational Models of …, 2014 | 8 | 2014 |
| Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights L Schweizer, P Seegerer, H Kim, R Saitenmacher, A Muench, L Barnick, ... Neuropathology and Applied Neurobiology 49 (1), e12866, 2023 | 5 | 2023 |
| New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning P Wagner, N Strodthoff, P Wurzel, A Marban, S Scharf, H Schäfer, ... Scientific Reports 12 (1), 18991, 2022 | 5 | 2022 |
| How to iNNvestigate neural networks' predictions! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... | 4 | 2018 |
| Pruning and/or quantizing machine learning predictors W Samek, S Lapuschkin, S Wiedemann, P Seegerer, Y Seul-Ki, K Mueller, ... US Patent App. 17/556,657, 2022 | 3 | 2022 |
| System and method for characterization of electrical properties of the heart from medical images and body surface potentials P Seegerer, T Mansi, MP Jolly, B Georgescu, A Kamen, D Comaniciu, ... US Patent 10,483,005, 2019 | 2 | 2019 |
| PATH-05. MACHINE LEARNING-BASED CYTOLOGICAL ANALYSIS OF CEREBROSPINAL FLUID IN MEDULLOBLASTOMA L Maack, C Kresbach, J Neumann, J Tischendorf, A Wefers, P Seegerer, ... Neuro-Oncology 26 (Suppl 4), 0, 2024 | | 2024 |
| Explainable machine learning in pathology-biomedical research and diagnostic application PE Seegerer PQDT-Global, 2023 | | 2023 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
