ArticleNature communications2021
Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 40 citations in OpenAlex.
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- Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.Scientific reports · 2025Article
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study.EBioMedicine · 2024Article
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
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- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
- Examination-Level Supervision for Deep Learning-based Intracranial Hemorrhage Detection on Head CT Scans.Radiology. Artificial intelligence · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Article
- Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.Nature biomedical engineering · 2023Article
- BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.Radiology. Artificial intelligence · 2023Article
- Artificial intelligence for nuclear medicine in oncology.Annals of nuclear medicine · 2022Review
- Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.Radiology. Artificial intelligence · 2022Article
- Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.Nature communications · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.