A causal perspective on dataset bias in machine learning for medical imaging
As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …
address fairness concerns becomes increasingly urgent. Despite considerable work …
Deep learning in breast cancer imaging: A decade of progress and future directions
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 …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows
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 …
radiomics and deep learning challenge radiology with requirements for world-wide …
TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images—a multi-center generalizability analysis
Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …
AI in medical imaging grand challenges: translation from competition to research benefit and patient care
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 …
decades. The recent evolution of AI into approaches such as deep learning has dramatically …
Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment
Objective Clinical and surgical decisions for glioblastoma patients depend on a tumor
imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance …
imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance …
PRIMIS: Privacy-preserving medical image sharing via deep sparsifying transform learning with obfuscation
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 …
sharing medical images across different centers. This is often a critical necessity in both …
Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges
Purpose To introduce developers to medical device regulatory processes and data
considerations in artificial intelligence and machine learning (AI/ML) device submissions …
considerations in artificial intelligence and machine learning (AI/ML) device submissions …
Deep learning-assisted multiple organ segmentation from whole-body CT images
Background: Automated organ segmentation from computed tomography (CT) images
facilitates a number of clinical applications, including clinical diagnosis, monitoring of …
facilitates a number of clinical applications, including clinical diagnosis, monitoring of …
Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy
Purpose Accurate dosimetry is critical for ensuring the safety and efficacy of
radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are …
radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are …