Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer
5
Zitationen
15
Autoren
2025
Jahr
Abstract
Our study indicates that the tested fully automated ML-based workflow is clinically feasible and leads to comparable results to conventional radiation treatment plans. This represents a promising step towards efficient and standardized prostate cancer treatment. Nevertheless, in the evaluated cohort, auto segmentation was associated with smaller target volumes compared to manual contours, highlighting the necessity of improving segmentation models and prospective testing of automation in radiation therapy.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.324 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.189 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.588 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.470 Zit.
Autoren
Institutionen
- Heidelberg University(DE)
- University Hospital Heidelberg(DE)
- Heidelberger Institut für Radioonkologie(DE)
- National Center for Tumor Diseases(DE)
- Klinik und Poliklinik für Strahlentherapie und Radioonkologie(DE)
- German Cancer Research Center(DE)
- DKFZ-ZMBH Alliance(DE)
- Kliniken Maria Hilf(DE)
- Hospital Clínico Universitario Lozano Blesa(ES)