[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

G Yang, Q Ye, J Xia - Information Fusion, 2022 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

A review on explainable artificial intelligence for healthcare: why, how, and when?

S Bharati, MRH Mondal… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …

Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

MS Heo, JE Kim, JJ Hwang, SS Han… - Dentomaxillofacial …, 2021 - academic.oup.com
Artificial intelligence, which has been actively applied in a broad range of industries in
recent years, is an active area of interest for many researchers. Dentistry is no exception to …

[HTML][HTML] Human, all too human? An all-around appraisal of the “artificial intelligence revolution” in medical imaging

F Coppola, L Faggioni, M Gabelloni… - Frontiers in …, 2021 - frontiersin.org
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a
niche super specialty computer application into a powerful tool which has revolutionized …

[HTML][HTML] Radiomics in breast MRI: Current progress toward clinical application in the era of artificial intelligence

H Satake, S Ishigaki, R Ito, S Naganawa - La radiologia medica, 2022 - Springer
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …

[HTML][HTML] Understanding sources of variation to improve the reproducibility of radiomics

B Zhao - Frontiers in Oncology, 2021 - frontiersin.org
Radiomics is the method of choice for investigating the association between cancer imaging
phenotype, cancer genotype and clinical outcome prediction in the era of precision …

[HTML][HTML] Introduction to radiomics for a clinical audience

C McCague, S Ramlee, M Reinius, I Selby, D Hulse… - Clinical Radiology, 2023 - Elsevier
Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

[HTML][HTML] Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

H Horng, A Singh, B Yousefi, EA Cohen, B Haghighi… - Scientific reports, 2022 - nature.com
Radiomic features have a wide range of clinical applications, but variability due to image
acquisition factors can affect their performance. The harmonization tool ComBat is a …

[HTML][HTML] Radiomics and machine learning applications in rectal cancer: current update and future perspectives

A Stanzione, F Verde, V Romeo… - World Journal of …, 2021 - ncbi.nlm.nih.gov
The high incidence of rectal cancer in both sexes makes it one of the most common tumors,
with significant morbidity and mortality rates. To define the best treatment option and …

[HTML][HTML] Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to …

L Fournier, L Costaridou, L Bidaut, N Michoux… - European …, 2021 - Springer
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue
characteristics and follow a well-understood path of technical, biological and clinical …