Zobrazeno 1 - 10
of 1 577
pro vyhledávání: '"P. Fietkau"'
Autor:
Huang, Yixing, Fan, Fuxin, Gomaa, Ahmed, Maier, Andreas, Fietkau, Rainer, Bert, Christoph, Putz, Florian
Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts t
Externí odkaz:
http://arxiv.org/abs/2409.08800
Autor:
Hou, Yihao, Bert, Christoph, Gomaa, Ahmed, Lahmer, Godehard, Hoefler, Daniel, Weissmann, Thomas, Voigt, Raphaela, Schubert, Philipp, Schmitter, Charlotte, Depardon, Alina, Semrau, Sabine, Maier, Andreas, Fietkau, Rainer, Huang, Yixing, Putz, Florian
Generating physician letters is a time-consuming task in daily clinical practice. This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner wit
Externí odkaz:
http://arxiv.org/abs/2408.10715
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
Publikováno v:
Radiotherapy & Oncology. 2024, 198, 110419, 1-8
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, 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 between 39 and 43 $^\circ$C for 60 minutes. Temper
Externí odkaz:
http://arxiv.org/abs/2310.01073
Publikováno v:
Discover Oncology, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Benign tumors, but rarely cancer, are common in patients with tuberous sclerosis complex (TSC). Blood samples from patients undergoing treatment for TSC at our institution were analyzed for their individual sensitivity to ionizing radiation.
Externí odkaz:
https://doaj.org/article/c7ac0964e00446a68786e2174acb3074
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