Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation

• URL: http://arxiv.org/abs/2306.12189v2
• Date: Mon, 29 Apr 2024 14:40:01 GMT
• Title: Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
• Authors: Lars Schmarje, Vasco Grossmann, Claudius Zelenka, Johannes Brünger, Reinhard Koch,
• Abstract summary: We introduce a novel strategy for generating high-quality labels in challenging datasets. We validate our methodology through a rigorous real-world test case in the biomedical field.
• Score: 2.5817162127446154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios. Current strategies, including semi-supervised learning and class blending, offer partial solutions but lack a definitive resolution. Addressing this gap, our paper introduces a novel strategy for generating high-quality labels in challenging datasets. Central to our approach is a clearly designed flowchart, based on a broad literature review, which enables the creation of reliable labels. We validate our methodology through a rigorous real-world test case in the biomedical field, specifically in deducing height reduction from vertebral imaging. Our empirical study, leveraging over 250,000 annotations, demonstrates the effectiveness of our strategies decisions compared to their alternatives.

Related papers

• A Systematic Literature Review on Multi-label Data Stream Classification [0.0]
  This systematic literature review presents an in-depth analysis of multi-label data stream classification proposals.<n>We characterize the latest methods in the literature, providing a comprehensive overview, building a thorough hierarchy, and discussing how the approach approaches each problem.<n>We identify the main gaps and offer recommendations for future research directions in the field.
  arXiv  Detail & Related papers  (2025-08-24T17:17:15Z)
• Progressively Exploring and Exploiting Cost-Free Data to Break Fine-Grained Classification Barriers [13.805180905579832]
  In this paper, we propose a novel learning paradigm to break the barriers in fine-grained classification.<n>This paradigm enables the model to progressively learn during inference, thereby leveraging cost-free data.<n> Experimental results demonstrate the general effectiveness of our method.
  arXiv  Detail & Related papers  (2024-12-29T07:11:44Z)
• The Impact of Semi-Supervised Learning on Line Segment Detection [11.636855122196323]
  We present a method for line segment detection in images, based on a semi-supervised framework. We show comparable results to fully supervised methods. Our method is to our knowledge the first to target line detection using modern state-of-the-art methodologies for semi-supervised learning.
  arXiv  Detail & Related papers  (2024-11-07T10:28:11Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions [10.502964056448283]
  Training models in medical imaging analysis typically require expensive and time-consuming collection of labeled data. We extensively investigated over 300 recent papers to provide a comprehensive overview of progress on label-efficient learning strategies in MIA. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies.
  arXiv  Detail & Related papers  (2023-03-22T11:51:49Z)
• Handling Image and Label Resolution Mismatch in Remote Sensing [10.009103959118931]
  We show how to handle resolution mismatch between overhead imagery and ground-truth label sources. We present a method that is supervised using low-resolution labels, but takes advantage of an exemplar set of high-resolution labels. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-supervised predictions.
  arXiv  Detail & Related papers  (2022-11-28T21:56:07Z)
• Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation [4.489713477369384]
  We introduce a novel adversarial learning-based semi-supervised segmentation method for medical image segmentation. Our method embeds both local and global features from multiple hidden layers and learns context relations between multiple classes. Our method outperforms current best-performing state-of-the-art semi-supervised learning approaches on the image segmentation of the left atrium (single class) and multiorgan datasets (multiclass)
  arXiv  Detail & Related papers  (2022-05-14T06:57:19Z)
• Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training [73.60883490436956]
  We propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification. We employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. We also design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples.
  arXiv  Detail & Related papers  (2022-05-10T07:50:08Z)
• Imposing Consistency for Optical Flow Estimation [73.53204596544472]
  Imposing consistency through proxy tasks has been shown to enhance data-driven learning. This paper introduces novel and effective consistency strategies for optical flow estimation.
  arXiv  Detail & Related papers  (2022-04-14T22:58:30Z)
• Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution [77.85461690214551]
  Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic differences. We propose a strategy to tackle Person Re-Identification and Text Authorship Attribution by enabling learning from unlabeled data even when samples from different classes are not prominently diverse.
  arXiv  Detail & Related papers  (2022-02-07T13:08:11Z)
• Region-level Active Learning for Cluttered Scenes [60.93811392293329]
  We introduce a new strategy that subsumes previous Image-level and Object-level approaches into a generalized, Region-level approach. We show that this approach significantly decreases labeling effort and improves rare object search on realistic data with inherent class-imbalance and cluttered scenes.
  arXiv  Detail & Related papers  (2021-08-20T14:02:38Z)
• Semi-Supervised Domain Generalization with Stochastic StyleMatch [90.98288822165482]
  In real-world applications, we might have only a few labels available from each source domain due to high annotation cost. In this work, we investigate semi-supervised domain generalization, a more realistic and practical setting. Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling.
  arXiv  Detail & Related papers  (2021-06-01T16:00:08Z)
• Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection [66.10057490293981]
  We propose a data-uncertainty guided multi-phase learning method for semi-supervised object detection. Our method behaves extraordinarily compared to baseline approaches and outperforms them by a large margin.
  arXiv  Detail & Related papers  (2021-03-29T09:27:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.