mlsanityData to 7 Oct 2026

Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection

arXiv 1809.02882 ↗First posted 8 Sep 2018cs.CV cs.LGcited by 28

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Abstract (arXiv)

Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling medical data makes this challenging. In this paper, we build a cost-sensitive active learning system for the problem of intracranial hemorrhage detection and segmentation on head computed tomography (CT). We show that our ensemble method compares favorably with the state-of-the-art, while running faster and using less memory. Moreover, our experiments are done using a substantially larger dataset than earlier papers on this topic. Since the labeling time could vary tremendously across examples, we model the labeling time and optimize the return on investment. We validate this idea by core-set selection on our large labeled dataset and by growing it with data from the wild.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2018.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 5; 2024: 2; 2025: 1.

28 papers.

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  1. 2021DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effortcited by 264
  2. 2022BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotationscited by 99
  3. 2021Semi-Supervised Active Learning with Temporal Output Discrepancycited by 50
  4. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
  5. 2022MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Imagescited by 38
  6. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  7. 2021DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentationcited by 27
  8. 2022Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuningcited by 21
  9. 2021Boosting Active Learning via Improving Test Performancecited by 19
  10. 2021MCDAL: Maximum Classifier Discrepancy for Active Learningcited by 18
  11. 2021A Simple Baseline for Low-Budget Active Learningcited by 15
  12. 2022Deep Active Learning with Noise Stabilitycited by 13
16 more
  1. 2022HandsOff: Labeled Dataset Generation With No Additional Human Annotationscited by 9
  2. 2022Self-supervised Assisted Active Learning for Skin Lesion Segmentationcited by 7
  3. 2021CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentationcited by 6
  4. 2021Towards General and Efficient Active Learningcited by 6
  5. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  6. 2024Bayesian Active Learning for Semantic Segmentationcited by 5
  7. 2023Learning to Explore Informative Trajectories and Samples for Embodied Perceptioncited by 3
  8. 2024Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Modelcited by 3
  9. 2021Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomographycited by 2
  10. 2022Rapid model transfer for medical image segmentation via iterative human-in-the-loop update: from labelled public to unlabelled clinical datasets for multi-organ segmentation in CTcited by 2
  11. 2022Temporal Output Discrepancy for Loss Estimation-based Active Learningcited by 2
  12. 2022Accelerating the creation of instance segmentation training sets through bounding box annotationcited by 1
  13. 2022Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECGcited by 1
  14. 2023Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Samplingcited by 0
  15. 2023Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detectioncited by 0
  16. 2025Boosting Active Learning with Knowledge Transfercited by 0