Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model …
A Barragán-Montero, A Bibal… - Physics in Medicine …, 2022 - iopscience.iop.org
The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …
the performance leap that occurred with new techniques of deep learning, convolutional …
Artificial general intelligence for radiation oncology
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As
prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can …
prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can …
Introduction to machine and deep learning for medical physicists
Recent years have witnessed tremendous growth in the application of machine learning
(ML) and deep learning (DL) techniques in medical physics. Embracing the current big data …
(ML) and deep learning (DL) techniques in medical physics. Embracing the current big data …
[HTML][HTML] Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines
Abstract Background and purpose The Global Quality Assurance of Radiation Therapy
Clinical Trials Harmonization Group (GHG) is a collaborative group of Radiation Therapy …
Clinical Trials Harmonization Group (GHG) is a collaborative group of Radiation Therapy …
Radiation therapy quality assurance tasks and tools: the many roles of machine learning
The recent explosion in machine learning efforts in the quality assurance (QA) space has
produced a variety of proofs‐of‐concept many with promising results. Expected outcomes of …
produced a variety of proofs‐of‐concept many with promising results. Expected outcomes of …
Benchmarking a foundation large language model on its ability to relabel structure names in accordance with the american association of physicists in medicine task …
Purpose To introduce the concept of using large language models (LLMs) to relabel
structure names in accordance with the American Association of Physicists in Medicine Task …
structure names in accordance with the American Association of Physicists in Medicine Task …
[HTML][HTML] A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels
Abstract Purpose: To present a Machine Learning pipeline for automatically relabeling
anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) …
anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) …
[HTML][HTML] Digital skills of therapeutic radiographers/radiation therapists–document analysis for a European educational curriculum
Introduction It is estimated that around 50% of cancer patients require Radiotherapy (RT) at
some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists …
some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists …
Integrated natural language processing and machine learning models for standardizing radiotherapy structure names
The lack of standardized structure names in radiotherapy (RT) data limits interoperability,
data sharing, and the ability to perform big data analysis. To standardize radiotherapy …
data sharing, and the ability to perform big data analysis. To standardize radiotherapy …
Standardising breast radiotherapy structure naming conventions: a machine learning approach
Simple Summary In radiotherapy treatment, organs at risk and target volumes are contoured
by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not …
by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not …