Publications (12)
Environment Scan of Generative AI Infrastructure for Clinical and Translational Science
Betina Idnay, Zihan Xu, William G. Adams +54
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 instituti…
Retrieval-Guided Generation for Safer Histopathology Image Captioning
Md. Enamul Hoq, Wataru Uegami, Saghir Alfasly +6
Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious is…
Highly accurate model for prediction of lung nodule malignancy with CT scans
Jason Causey, Junyu Zhang, Shiqian Ma +6
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It rem…
Virtual-Eyes: Quantitative Validation of a Lung CT Quality-Control Pipeline for Foundation-Model Cancer Risk Prediction
Md. Enamul Hoq, Linda Larson-Prior, Fred Prior
Robust preprocessing is rarely quantified in deep-learning pipelines for low-dose CT (LDCT) lung cancer screening. We develop and validate Virtual-Eyes, a clinically motivated 16-b…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations
David A. Clunie, Adam Flanders, Adam Taylor +16
This report addresses the technical aspects of de-identification of medical images of human subjects and biospecimens, such that re-identification risk of ethical, moral, and legal…
medigan: a Python library of pretrained generative models for medical image synthesis
Richard Osuala, Grzegorz Skorupko, Noussair Lazrak +9
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging. However, there is (1) limited ava…
Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
Michael W. Rutherford, Tracy Nolan, Linmin Pei +10
Medical imaging research increasingly depends on large-scale data sharing to promote reproducibility and train Artificial Intelligence (AI) models. Ensuring patient privacy remains…
Lung cancer screening with low-dose CT scans using a deep learning approach
Jason L. Causey, Yuanfang Guan, Wei Dong +4
Lung cancer is the leading cause of cancer deaths. Early detection through low-dose computed tomography (CT) screening has been shown to significantly reduce mortality but suffers…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Enabling Global Image Data Sharing in the Life Sciences
Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner +20
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we tak…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…