Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
M Cobo, P Menéndez Fernández-Miranda… - Scientific data, 2023 - nature.com
Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide …
Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …
Artificial intelligence (AI), in one form or another, has been a part of medical imaging for decades. The recent evolution of AI into approaches such as deep learning has dramatically …
A Bianconi, LF Rossi, M Bonada, P Zeppa, E Nico… - Brain Informatics, 2023 - Springer
Objective Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance …
Objective: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both …
Purpose To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions …
Background: Automated organ segmentation from computed tomography (CT) images facilitates a number of clinical applications, including clinical diagnosis, monitoring of …
Purpose Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are …