ArticleJournal of medical imaging (Bellingham, Wash.)2018
DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.
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Who cites it
144 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.Frontiers in neuroinformatics · 2020Pooled it
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- Efficient transformer integration in nnU-Net for liver tumor segmentation: an external validation study.BMC medical imaging · 2026Article
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
- A Two-Phase Deep Learning Approach for Architectural Distortion Detection in Mammograms.Journal of imaging informatics in medicine · 2026Article
- Development of a No-Reference CT Image Quality Assessment Method Using RadImageNet Pre-trained Deep Learning Models.Journal of imaging informatics in medicine · 2026Article
- Contextual structured annotations on PACS: a futuristic vision for reporting routine oncologic imaging studies and its potential to transform clinical work and research.Abdominal radiology (New York) · 2026Review
- Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Multi-task learning for predicting pulmonary nodule growth and follow-up volume.Frontiers in oncology · 2026Article
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- AAPM CT metal artifact reduction grand challenge.Medical physics · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- Large-vocabulary segmentation for medical images with text prompts.NPJ digital medicine · 2025Article
- ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet.BMC medical imaging · 2025Article
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- Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.Scientific reports · 2025Article
- The Duke Lung Cancer Screening (DLCS) Dataset: A Reference Dataset of Annotated Low-Dose Screening Thoracic CT.Radiology. Artificial intelligence · 2025Article
- Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.International forum of allergy & rhinology · 2025Article
84 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals' picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset. Our process is scalable and requires minimum manual annotation effort. We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications. Using DeepLesion, we train a universal lesion detector that can find all types of lesions with one unified framework. In this challenging task, the proposed lesion detector achieves a sensitivity of 81.1% with five false positives per image.
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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.