Zobrazeno 1 - 10
of 670
pro vyhledávání: '"GOMAA, AHMED"'
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
Publikováno v:
Foundations and Trends in Information Systems: Vol. 7: No. 4, pp 310-356 (2024)
This paper provides a comprehensive analysis of the challenges and controversies associated with blockchain technology. It identifies technical challenges such as scalability, security, privacy, and interoperability, as well as business and adoption
Externí odkaz:
http://arxiv.org/abs/2409.06179
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
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:
Ahmed Gomaa Ahmed Elsayed, Dina F. Badr, Nermene Youssef Abo El Kheir, Maysaa El Sayed Zaki, Abdelrahman Eid Mahmoud Mossad, Ehab Mohammed Fahmy Mahmoud
Publikováno v:
Italian Journal of Pediatrics, Vol 50, Iss 1, Pp 1-8 (2024)
Abstract Background Gram-negative bacilli represents an important pathogen in hospital-acquired infections (HAIs) worldwide. The emergence of antibiotic resistance in these pathogens warrants attention for the proper management of infections. Extende
Externí odkaz:
https://doaj.org/article/559dbdff35e844de8c59ac3f3385bc3b
