mlsanityData to 7 Oct 2026

EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation

arXiv 2307.10745 ↗First posted 20 Jul 2023cs.CVcited by 2

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Abstract (arXiv)

Active learning algorithms have become increasingly popular for training models with limited data. However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data. To address this issue, we propose EdgeAL, which utilizes the edge information of unseen images as it a priori information for measuring uncertainty. The uncertainty is quantified by analyzing the divergence and entropy in model predictions across edges. This measure is then used to select superpixels for annotation. We demonstrate the effectiveness of EdgeAL on multi-class Optical Coherence Tomography (OCT) segmentation tasks, where we achieved a 99% dice score while reducing the annotation label cost to 12%, 2.3%, and 3%, respectively, on three publicly available datasets (Duke, AROI, and UMN). The source code is available at urlhttps://github.com/Mak-Ta-Reque/EdgeAL

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

17 of 30 references.

References · 17

  1. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  2. 2017Deep Bayesian Active Learning with Image Datacited by 812
  3. 2019Variational Adversarial Active Learningcited by 284
  4. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 211
  5. 2017Cost-Effective Active Learning for Deep Image Classificationcited by 165
  6. 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 145
  7. 2020Regularizing Class-wise Predictions via Self-knowledge Distillationcited by 144
  8. 2019ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentationcited by 95
  9. 2013SEEDS: Superpixels Extracted via Energy-Driven Samplingcited by 75
  10. 2018CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentationcited by 47
  11. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
  12. 2017Cost-Effective Active Learning for Melanoma Segmentationcited by 40
5 more
  1. 2022Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuningcited by 21
  2. 2020Suggestive Annotation of Brain Tumour Images with Gradient-guided Samplingcited by 19
  3. 2022Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentationcited by 19
  4. 2018A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancercited by 12
  5. 2022DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Gradingcited by 3

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  2. 2024Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Projectcited by 0