A comprehensive review on federated learning based models for healthcare applications
A disease is an abnormal condition that negatively impacts the functioning of the human
body. Pathology determines the causes behind the disease and identifies its development …
body. Pathology determines the causes behind the disease and identifies its development …
A review of medical federated learning: Applications in oncology and cancer research
A Chowdhury, H Kassem, N Padoy, R Umeton… - International MICCAI …, 2021 - Springer
Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related
death in children. The five-year survival rate for high-grade gliomas in children is less than …
death in children. The five-year survival rate for high-grade gliomas in children is less than …
[HTML][HTML] The brain tumor segmentation (brats) challenge 2023: glioma segmentation in sub-saharan Africa patient population (brats-africa)
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively
rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years …
rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years …
Developing medical imaging AI for emerging infectious diseases
Advances in artificial intelligence (AI) and computer vision hold great promise for assisting
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and
clinical communities. However, greater expertise is required to develop DL algorithms, and …
clinical communities. However, greater expertise is required to develop DL algorithms, and …
Privacy preservation for federated learning in health care
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …
models that can inform clinical workflows. However, access to large quantities of diverse …
Artificial intelligence applications in histopathology
Histopathology is a vital diagnostic discipline in medicine, fundamental to our
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
On the Conflict of Robustness and Learning in Collaborative Machine Learning
M Raynal, C Troncoso - arXiv preprint arXiv:2402.13700, 2024 - arxiv.org
Collaborative Machine Learning (CML) allows participants to jointly train a machine learning
model while keeping their training data private. In scenarios where privacy is a strong …
model while keeping their training data private. In scenarios where privacy is a strong …
Artificial intelligence in coronary artery calcium measurement: Barriers and solutions for implementation into daily practice
T Yamaoka, S Watanabe - European Journal of Radiology, 2023 - Elsevier
Coronary artery calcification (CAC) measurement is a valuable predictor of cardiovascular
risk. However, its measurement can be time-consuming and complex, thus driving the desire …
risk. However, its measurement can be time-consuming and complex, thus driving the desire …