Preparing medical imaging data for machine learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …
potential applications are vast and include the entirety of the medical imaging life cycle from …
Federated learning in a medical context: a systematic literature review
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to
abide by the existing privacy regulations to preserve patients' anonymity. However, data is …
abide by the existing privacy regulations to preserve patients' anonymity. However, data is …
[HTML][HTML] Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI)
field. Modern radiation oncology is based on the exploitation of advanced computational …
field. Modern radiation oncology is based on the exploitation of advanced computational …
[HTML][HTML] Global healthcare fairness: We should be sharing more, not less, data
The availability of large, deidentified health datasets has enabled significant innovation in
using machine learning (ML) to better understand patients and their diseases. However …
using machine learning (ML) to better understand patients and their diseases. However …
Deep learning workflow in radiology: a primer
E Montagnon, M Cerny, A Cadrin-Chênevert… - Insights into …, 2020 - Springer
Interest for deep learning in radiology has increased tremendously in the past decade due to
the high achievable performance for various computer vision tasks such as detection …
the high achievable performance for various computer vision tasks such as detection …
[HTML][HTML] Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
The vast amount of data produced by today's medical imaging systems has led medical
professionals to turn to novel technologies in order to efficiently handle their data and exploit …
professionals to turn to novel technologies in order to efficiently handle their data and exploit …
AAPM task group report 273: recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep
learning (DL) techniques, have enabled broad application of these methods in health care …
learning (DL) techniques, have enabled broad application of these methods in health care …
[HTML][HTML] Federated learning in medical imaging: part II: methods, challenges, and considerations
E Darzidehkalani, M Ghasemi-Rad… - Journal of the American …, 2022 - Elsevier
Federated learning is a machine learning method that allows decentralized training of deep
neural networks among multiple clients while preserving the privacy of each client's data …
neural networks among multiple clients while preserving the privacy of each client's data …
Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges
Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a
clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing …
clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing …
A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data
B Ocaña-Tienda, J Pérez-Beteta, JD Villanueva-García… - Scientific data, 2023 - nature.com
Brain metastasis (BM) is one of the main complications of many cancers, and the most
frequent malignancy of the central nervous system. Imaging studies of BMs are routinely …
frequent malignancy of the central nervous system. Imaging studies of BMs are routinely …