ArticleFrontiers in radiology2021
Active Learning Performance in Labeling Radiology Images Is 90% Effective.
Article in Frontiers in radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence for postoperative plain radiographic assessment after reverse total shoulder arthroplasty: current evidence, clinical readiness, and limitations.Clinics in shoulder and elbow · 2026Article
- Streamlining the annotation process by radiologists of volumetric medical images with few-shot learning.International journal of computer assisted radiology and surgery · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies.North American Spine Society journal · 2024Article
- Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.BMC medical imaging · 2024Article
- Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.World journal of radiology · 2023Article
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Authors and funding
5 authors.
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Abstract
To train artificial intelligence (AI) systems on radiology images, an image labeling step is necessary. Labeling for radiology images usually involves a human radiologist manually drawing a (polygonal) shape onto the image and attaching a word to it. As datasets are typically large, this task is repetitive, time-consuming, error-prone, and expensive. The AI methodology of active learning (AL) can assist human labelers by continuously sorting the unlabeled images in order of information gain and thus getting the labeler always to label the most informative image next. We find that after about 10%, depending on the dataset, of the images in a realistic dataset are labeled, virtually all the information content has been learnt and the remaining images can be automatically labeled. These images can then be checked by the radiologist, which is far easier and faster to do. In this way, the entire dataset is labeled with much less human effort. We introduce AL in detail and expose the effectiveness using three real-life datasets. We contribute five distinct elements to the standard AL workflow creating an advanced methodology.
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