AIMC Topic: Radiation Oncology
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Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases.
Anticancer research
Mar 1, 2021
BACKGROUND/AIM: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called Bone Metastases Ensemble Trees for Survival (BMETS). This prediction model is based on a machine lear...
[12. Application of Deep Learning in Radiotherapy Imaging].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2021
[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2021
PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this condition is difficult to provide because the clinical evaluation of radiation dermatitis is made by...
The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.
Technology in cancer research & treatment
Jan 1, 2021
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation o...
Artificial intelligence and radiomics for radiation oncology.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Oct 1, 2020
Artificial Intelligence and the Radiographer/Radiological Technologist Profession: A joint statement of the International Society of Radiographers and Radiological Technologists and the European Federation of Radiographer Societies.
Radiography (London, England : 1995)
May 1, 2020
Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.
Journal of the American College of Radiology : JACR
Sep 1, 2019
Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, ...
Radiation Oncology Terminology Linker: A Step Towards a Linked Data Knowledge Base.
Studies in health technology and informatics
Jan 1, 2018
Performing image feature extraction in radiation oncology is often dependent on the organ and tumor delineations provided by clinical staff. These delineation names are free text DICOM metadata fields resulting in undefined information, which require...
On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology.
International journal of radiation oncology, biology, physics
Jan 1, 2018
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