| Radiomics of brain MRI: utility in prediction of metastatic tumor type HC Kniep, F Madesta, T Schneider, U Hanning, MH Schönfeld, G Schön, ... Radiology 290 (2), 479-487, 2019 | 195 | 2019 |
| Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting N Gessert, T Sentker, F Madesta, R Schmitz, H Kniep, I Baltruschat, ... IEEE Transactions on Biomedical Engineering 67 (2), 495-503, 2019 | 133 | 2019 |
| PAIP 2019: Liver cancer segmentation challenge YJ Kim, H Jang, K Lee, S Park, SG Min, C Hong, JH Park, K Lee, J Kim, ... Medical image analysis 67, 101854, 2021 | 85 | 2021 |
| Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting N Gessert, T Sentker, F Madesta, R Schmitz, H Kniep, I Baltruschat, ... arXiv preprint arXiv:1808.01694, 2018 | 85 | 2018 |
| Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture R Schmitz, F Madesta, M Nielsen, J Krause, S Steurer, R Werner, T Rösch Medical image analysis 70, 101996, 2021 | 73 | 2021 |
| GDL-FIRE: Deep learning-based fast 4D CT image registration T Sentker, F Madesta, R Werner International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 60 | 2018 |
| 4D CT image artifacts affect local control in SBRT of lung and liver metastases T Sentker, V Schmidt, AK Ozga, C Petersen, F Madesta, C Hofmann, ... Radiotherapy and Oncology 148, 229-234, 2020 | 37 | 2020 |
| Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction F Madesta, T Sentker, T Gauer, R Werner Medical physics 47 (11), 5619-5631, 2020 | 24 | 2020 |
| Intelligent 4D CT sequence scanning (i4DCT): Concept and performance evaluation R Werner, T Sentker, F Madesta, T Gauer, C Hofmann Medical physics 46 (8), 3462-3474, 2019 | 24 | 2019 |
| Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT R Werner, T Sentker, F Madesta, A Schwarz, M Vornehm, T Gauer, ... Medical Physics 47 (6), 2408-2412, 2020 | 16 | 2020 |
| Comparison of intelligent 4D CT sequence scanning and conventional spiral 4D CT: a first comprehensive phantom study R Werner, J Szkitsak, T Sentker, F Madesta, A Schwarz, S Fernolendt, ... Physics in Medicine & Biology 66 (1), 015004, 2021 | 15 | 2021 |
| Skin Lesion Diagnosis using Ensembles N Gessert, T Sentker, F Madesta, R Schmitz, H Kniep, I Baltruschat, ... Unscaled Multi-Crop Evaluation, and Loss Weighting. available on:[https …, 2018 | 8 | 2018 |
| Combining good old random forest and DeepLabv3+ for ISLES 2018 CT-based stroke segmentation L Böhme, F Madesta, T Sentker, R Werner Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 7 | 2019 |
| Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1 I Ristow, F Madesta, L Well, F Shenas, F Wright, I Molwitz, S Farschtschi, ... Neuro-oncology 24 (10), 1790-1798, 2022 | 6 | 2022 |
| Technical considerations for automated low-pitch spiral 4D CT scanning protocol selection R Werner, T Sothmann, F Madesta, T Gauer, C Hofmann Medical Imaging 2018: Physics of Medical Imaging 10573, 1011-1016, 2018 | 5 | 2018 |
| Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies F Madesta, R Schmitz, T Rösch, R Werner Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 4 | 2020 |
| Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction F Madesta, T Gauer, T Sentker, R Werner Medical Imaging 2019: Image Processing 10949, 1094902, 2019 | 4 | 2019 |
| Clinical application of breathing-adapted 4D CT: image quality comparison to conventional 4D CT R Werner, J Szkitsak, F Madesta, L Büttgen, L Wimmert, T Sentker, ... Strahlentherapie und Onkologie 199 (7), 686-691, 2023 | 2 | 2023 |
| Discordant and converting receptor expressions in brain metastases from breast cancer: MRI-based non-invasive receptor status tracking A Heitkamp, F Madesta, S Amberg, S Wahaj, T Schröder, M Bechstein, ... Cancers 15 (11), 2880, 2023 | 1 | 2023 |
| Deep learning‐based conditional inpainting for restoration of artifact‐affected 4D CT images F Madesta, T Sentker, T Gauer, R Werner Medical Physics, 2023 | 1 | 2023 |
Rene WernerUniversity Medical Center Hamburg-Eppendorf, Center for Biomedical Artificial IntelligenceVerified email at uke.de
