mlsanityData to 7 Oct 2026

Task-Aware Active Learning for Endoscopic Image Analysis

arXiv 2204.03440 ↗First posted 7 Apr 2022cs.CVcited by 4

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Abstract (arXiv)

Semantic segmentation of polyps and depth estimation are two important research problems in endoscopic image analysis. One of the main obstacles to conduct research on these research problems is lack of annotated data. Endoscopic annotations necessitate the specialist knowledge of expert endoscopists and due to this, it can be difficult to organise, expensive and time consuming. To address this problem, we investigate an active learning paradigm to reduce the number of training examples by selecting the most discriminative and diverse unlabelled examples for the task taken into consideration. Most of the existing active learning pipelines are task-agnostic in nature and are often sub-optimal to the end task. In this paper, we propose a novel task-aware active learning pipeline and applied for two important tasks in endoscopic image analysis: semantic segmentation and depth estimation. We compared our method with the competitive baselines. From the experimental results, we observe a substantial improvement over the compared baselines. Codes are available at https://github.com/thetna/endo-active-learn.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

7 of 19 references.

References · 7

  1. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  2. 2019Variational Adversarial Active Learningcited by 284
  3. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 150
  4. 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 145
  5. 2018Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Networkcited by 74
  6. 2020Sequential Graph Convolutional Network for Active Learningcited by 69
  7. 2019An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysiscited by 11

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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy datacited by 6
  2. 2024Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severitycited by 3
  3. 2023M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Taskscited by 2
  4. 2025Endoscopic Depth Estimation Based on Deep Learning: A Surveycited by 1