mlsanityData to 14 Oct 2026

Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project

arXiv 2403.15143 ↗First posted 22 Mar 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segmentation algorithms have completely transformed this process and made it possible to automate image segmentation. By accurately segmenting medical images, these algorithms can greatly minimize the time and effort necessary for manual annotation. Additionally, by incorporating Active Learning (AL) methods, these segmentation algorithms can perform far more effectively with a smaller amount of ground truth data. We introduce MedDeepCyleAL, an end-to-end framework implementing the complete AL cycle. It provides researchers with the flexibility to choose the type of deep learning model they wish to employ and includes an annotation tool that supports the classification and segmentation of medical images. The user-friendly interface allows for easy alteration of the AL and DL model settings through a configuration file, requiring no prior programming experience. While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 22 Mar 2024.

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23 of 33 references.

References · 23

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  12. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 45
11 more
  1. 2021Reducing Label Effort: Self-Supervised meets Active Learningcited by 42
  2. 2017Cost-Effective Active Learning for Melanoma Segmentationcited by 41
  3. 2022Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuningcited by 21
  4. 2023Active learning for medical image segmentation with stochastic batchescited by 21
  5. 2020Suggestive Annotation of Brain Tumour Images with Gradient-guided Samplingcited by 19
  6. 2022Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentationcited by 19
  7. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  8. 2018A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancercited by 12
  9. 2023EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentationcited by 3
  10. 2023Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Imagescited by 3
  11. 2020HUMAN: Hierarchical Universal Modular ANnotatorcited by 2

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