Optimizing Resources for On-the-Fly Label Estimation with Multiple Unknown Medical Experts

• URL: http://arxiv.org/abs/2510.03954v1
• Date: Sat, 04 Oct 2025 21:41:26 GMT
• Title: Optimizing Resources for On-the-Fly Label Estimation with Multiple Unknown Medical Experts
• Authors: Tim Bary, Tiffanie Godelaine, Axel Abels, Benoît Macq,
• Abstract summary: We propose an adaptive approach for real-time annotation that supports on-the-fly labeling of incoming data.<n>We evaluate our approach on three multi-annotator classification datasets across different modalities.
• Score: 2.904892426557913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate ground truth estimation in medical screening programs often relies on coalitions of experts and peer second opinions. Algorithms that efficiently aggregate noisy annotations can enhance screening workflows, particularly when data arrive continuously and expert proficiency is initially unknown. However, existing algorithms do not meet the requirements for seamless integration into screening pipelines. We therefore propose an adaptive approach for real-time annotation that (I) supports on-the-fly labeling of incoming data, (II) operates without prior knowledge of medical experts or pre-labeled data, and (III) dynamically queries additional experts based on the latent difficulty of each instance. The method incrementally gathers expert opinions until a confidence threshold is met, providing accurate labels with reduced annotation overhead. We evaluate our approach on three multi-annotator classification datasets across different modalities. Results show that our adaptive querying strategy reduces the number of expert queries by up to 50% while achieving accuracy comparable to a non-adaptive baseline. Our code is available at https://github.com/tbary/MEDICS

Related papers

• MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation [55.37355146924576]
  MedSeqFT is a sequential fine-tuning framework for medical image analysis.<n>It adapts pre-trained models to new tasks while refining their representational capacity.<n>It consistently outperforms state-of-the-art fine-tuning strategies.
  arXiv  Detail & Related papers  (2025-09-07T15:22:53Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• Principled Bayesian Optimisation in Collaboration with Human Experts [23.988732776208053]
  We consider a setup where experts provide advice through binary accept/reject recommendations (labels) Experts' labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable. We introduce the first principled approach that provides two key guarantees.
  arXiv  Detail & Related papers  (2024-10-14T12:46:02Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use? [6.144041824426555]
  We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to meet the standards of an everyday clinical device.
  arXiv  Detail & Related papers  (2023-05-03T09:38:52Z)
• A Gold Standard Dataset for the Reviewer Assignment Problem [70.45113777449373]
  "Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper.<n>Key challenge in comparing existing algorithms and developing better algorithms is the lack of publicly available gold-standard data.<n>We collect a novel dataset of similarity scores that we release to the research community.
  arXiv  Detail & Related papers  (2023-03-23T16:15:03Z)
• Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification [12.622412402489951]
  An ensemble technique combines local predictions from Gaussian experts trained on different partitions of the data. This paper proposes a flexible expert selection approach based on the characteristics of entry data points.
  arXiv  Detail & Related papers  (2022-11-17T23:23:26Z)
• Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis [0.31317409221921133]
  In medical imaging analysis, multiple expert annotators often produce subjective estimates about "ground truth labels" We propose a simple yet effective approach to combine annotations from multiple radiology experts for training a deep learning-based detector.
  arXiv  Detail & Related papers  (2022-03-20T17:57:26Z)
• Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [9.745971699005857]
  We propose a novel uncertainty-guided mutual consistency learning framework for medical image segmentation. It integrates intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. Our method achieves performance gains by leveraging unlabeled data and outperforms existing semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2021-12-05T08:19:41Z)
• A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
  Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
  arXiv  Detail & Related papers  (2021-09-20T13:40:21Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Semi-supervised lung nodule retrieval [2.055949720959582]
  A content based image retrieval (CBIR) system provides as its output a set of images, ranked by similarity to the query image. Ground truth on similarity between dataset elements (e.g. between nodules) is not readily available, thus greatly challenging machine learning methods. The current study suggests a semi-supervised approach that involves two steps: 1) Automatic annotation of a given partially labeled dataset; 2) Learning a semantic similarity metric space based on the predicated annotations. The proposed system is demonstrated in lung nodule retrieval using the LIDC dataset, and shows that it is feasible to learn embedding from predicted ratings.
  arXiv  Detail & Related papers  (2020-05-04T19:26:14Z)

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.