Zobrazeno 1 - 10
of 530
pro vyhledávání: '"Bert, Christoph"'
Autor:
Gomaa, Ahmed, Huang, Yixing, Hagag, Amr, Schmitter, Charlotte, Höfler, Daniel, Weissmann, Thomas, Breininger, Katharina, Schmidt, Manuel, Stritzelberger, Jenny, Delev, Daniel, Coras, Roland, Dörfler, Arnd, Schnell, Oliver, Frey, Benjamin, Gaipl, Udo S., Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, Putz, Florian
Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Me
Externí odkaz:
http://arxiv.org/abs/2405.12963
Autor:
Huang, Yixing, Khodabakhshi, Zahra, Gomaa, Ahmed, Schmidt, Manuel, Fietkau, Rainer, Guckenberger, Matthias, Andratschke, Nicolaus, Bert, Christoph, Tanadini-Lang, Stephanie, Putz, Florian
Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without for
Externí odkaz:
http://arxiv.org/abs/2405.10870
Autor:
Khatun, Rupali, Chatterjee, Soumick, Bert, Christoph, Wadepohl, Martin, Schmidt, Manfred, Ott, Oliver J., Fietkau, Rainer, Nürnberger, Andreas, Gaipl, Udo S., Frey, Benjamin
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures of 39 to 43 $\degree$C for 60 minutes. Temperature
Externí odkaz:
http://arxiv.org/abs/2310.01073
Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration. Naive weight transfer methods share intermediate model
Externí odkaz:
http://arxiv.org/abs/2309.17192
Autor:
Hagag, Amr, Gomaa, Ahmed, Kornek, Dominik, Maier, Andreas, Fietkau, Rainer, Bert, Christoph, Putz, Florian, Huang, Yixing
Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imagi
Externí odkaz:
http://arxiv.org/abs/2306.14596
Autor:
Huang, Yixing, Gomaa, Ahmed, Semrau, Sabine, Haderlein, Marlen, Lettmaier, Sebastian, Weissmann, Thomas, Grigo, Johanna, Tkhayat, Hassen Ben, Frey, Benjamin, Gaipl, Udo S., Distel, Luitpold V., Maier, Andreas, Fietkau, Rainer, Bert, Christoph, Putz, Florian
The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this wo
Externí odkaz:
http://arxiv.org/abs/2304.11957
Autor:
Putz, Florian, Grigo, Johanna, Weissmann, Thomas, Schubert, Philipp, Hoefler, Daniel, Gomaa, Ahmed, Tkhayat, Hassen Ben, Hagag, Amr, Lettmaier, Sebastian, Frey, Benjamin, Gaipl, Udo S., Distel, Luitpold V., Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, Huang, Yixing
Background: Tumor segmentation in MRI is crucial in radiotherapy (RT) treatment planning for brain tumor patients. Segment anything (SA), a novel promptable foundation model for autosegmentation, has shown high accuracy for multiple segmentation task
Externí odkaz:
http://arxiv.org/abs/2304.07875
Autor:
Sommer, Philipp, Huang, Yixing, Bert, Christoph, Maier, Andreas, Schmidt, Manuel, Dörfler, Arnd, Fietkau, Rainer, Putz, Florian
Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth.
Externí odkaz:
http://arxiv.org/abs/2302.08802
Autor:
Weissmann, Thomas, Huang, Yixing, Fischer, Stefan, Roesch, Johannes, Mansoorian, Sina, Gaona, Horacio Ayala, Gostian, Antoniu-Oreste, Hecht, Markus, Lettmaier, Sebastian, Deloch, Lisa, Frey, Benjamin, Gaipl, Udo S., Distel, Luitpold V., Maier, Andreas, Iro, Heinrich, Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, Putz, Florian
Publikováno v:
Front. Oncol. 13:1115258
Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. Methods: An expert-delineat
Externí odkaz:
http://arxiv.org/abs/2208.13224
Autor:
Huang, Yixing, Bert, Christoph, Fischer, Stefan, Schmidt, Manuel, Dörfler, Arnd, Maier, Andreas, Fietkau, Rainer, Putz, Florian
Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing interm
Externí odkaz:
http://arxiv.org/abs/2204.13591