Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial in...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...
The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...
The prevalence of cancer is an increasing healthcare issue as it is the predominant cause of death w...
Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment...
Machine learning (ML) applications in medicine represent an emerging field of research with the pote...
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155503/1/mp13570_am.pdfhttps://deepblu...
The possible uses of artificial intelligence (AI) in radiation oncology are diverse and wide ranging...
The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of techno...
Artificial Intelligence (AI) has been widely employed in the medical field in recent years in such a...
Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its ...
In this big-data era, like every other field, healthcare is also turning towards artificial intellig...
This thesis focuses on healthcare (Big) Data and the value of interpretable models for outcome predi...
International audienceAn increasing number of parameters can be considered when making decisions in ...
Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has r...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...
The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...
The prevalence of cancer is an increasing healthcare issue as it is the predominant cause of death w...
Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment...
Machine learning (ML) applications in medicine represent an emerging field of research with the pote...
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155503/1/mp13570_am.pdfhttps://deepblu...
The possible uses of artificial intelligence (AI) in radiation oncology are diverse and wide ranging...
The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of techno...
Artificial Intelligence (AI) has been widely employed in the medical field in recent years in such a...
Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its ...
In this big-data era, like every other field, healthcare is also turning towards artificial intellig...
This thesis focuses on healthcare (Big) Data and the value of interpretable models for outcome predi...
International audienceAn increasing number of parameters can be considered when making decisions in ...
Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has r...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...
The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays...
Artificial intelligence, and in particular deep learning using convolutional neural networks, has be...