# Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection

arXiv: https://arxiv.org/abs/1809.02882 · HTML: https://mlsanity.com/paper/1809.02882/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 28 of 28)

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- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
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- [Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuning](https://mlsanity.com/paper/2209.06285/) (2022)
- [Boosting Active Learning via Improving Test Performance](https://mlsanity.com/paper/2112.05683/) (2021)
- [MCDAL: Maximum Classifier Discrepancy for Active Learning](https://mlsanity.com/paper/2107.11049/) (2021)
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- [Deep Active Learning with Noise Stability](https://mlsanity.com/paper/2205.13340/) (2022)
- [HandsOff: Labeled Dataset Generation With No Additional Human Annotations](https://mlsanity.com/paper/2212.12645/) (2022)
- [Self-supervised Assisted Active Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2205.07021/) (2022)
- [CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2112.05975/) (2021)
- [Towards General and Efficient Active Learning](https://mlsanity.com/paper/2112.07963/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Bayesian Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2408.01694/) (2024)
- [Learning to Explore Informative Trajectories and Samples for Embodied Perception](https://mlsanity.com/paper/2303.10936/) (2023)
- [Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model](https://mlsanity.com/paper/2407.20461/) (2024)
- [Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography](https://mlsanity.com/paper/2102.12040/) (2021)
- [Rapid 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 CT](https://mlsanity.com/paper/2204.06243/) (2022)
- [Temporal Output Discrepancy for Loss Estimation-based Active Learning](https://mlsanity.com/paper/2212.10613/) (2022)
- [Accelerating the creation of instance segmentation training sets through bounding box annotation](https://mlsanity.com/paper/2205.11563/) (2022)
- [Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG](https://mlsanity.com/paper/2205.14508/) (2022)
- [Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling](https://mlsanity.com/paper/2309.16139/) (2023)
- [Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection](https://mlsanity.com/paper/2310.08387/) (2023)
- [Boosting Active Learning with Knowledge Transfer](https://mlsanity.com/paper/2509.15805/) (2025)

## 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.