Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation

• URL: http://arxiv.org/abs/2606.16325v1
• Date: Mon, 15 Jun 2026 07:31:08 GMT
• Title: Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
• Abstract summary: Few-shot medical image segmentation methods typically assume a single ground-truth annotation.<n>We propose an attention-based prototype calibration framework for few-shot multi-rater segmentation.
• Score: 4.310867444150861
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Few-shot medical image segmentation methods typically assume a single ground-truth annotation, overlooking systematic variability across expert raters commonly observed in clinical datasets. We propose an attention-based prototype calibration framework for few-shot multi-rater segmentation that models rater-specific deviations from a consensus representation in prototype space. A lightweight yet principled attention operator directly refines rater prototypes without modifying the backbone feature extractor, making the approach fully compatible with existing prototype-based few-shot segmentation methods. This design preserves semantic consistency while enabling personalized segmentation outputs with minimal computational overhead. Experiments on multi-rater medical imaging datasets demonstrate consistent improvements over baseline prototype approaches, highlighting the effectiveness of structured prototype calibration for modeling annotation variability. Our code is available at https://github.com/truong2710-cyber/JAPC.

Related papers

• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Image Segmentation [66.82598255715696]
  Federated learning enables multiple medical institutions to train a global model without sharing data.<n>Current approaches primarily focus on final-layer features, overlooking critical multi-level cues.<n>We propose FedBCS to bridge feature representation gaps via domain-invariant contextual prototypes alignment.
  arXiv  Detail & Related papers  (2025-11-14T04:15:34Z)
• Proto-Former: Unified Facial Landmark Detection by Prototype Transformer [77.47431726595111]
  Proto-Former is a unified, adaptive, end-to-end facial landmark detection framework.<n>It enables joint training across multiple datasets within a unified architecture.<n>Proto-Former achieves superior performance compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-17T06:00:25Z)
• DPL: Spatial-Conditioned Diffusion Prototype Enhancement for One-Shot Medical Segmentation [0.0]
  One-shot medical image segmentation faces fundamental challenges in prototype representation due to limited annotated data and anatomical variability across patients.<n>Traditional prototype-based methods rely on deterministic averaging of support features, creating brittle representations that fail to capture intra-class diversity essential for robust generalization.<n>This work introduces Diffusion Prototype Learning, a novel framework that reformulates prototype construction through diffusion-based feature space exploration.
  arXiv  Detail & Related papers  (2025-10-14T05:28:58Z)
• Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation [75.18058114915327]
  Generalized Few-Shot Semanticnative (GFSS) aims to extend a segmentation model to novel classes with only a few annotated examples.<n>We propose FewCLIP, a probabilistic prototype calibration framework over multi-modal prototypes from the pretrained CLIP.<n>We show FewCLIP significantly outperforms state-of-the-art approaches across both GFSS and class-incremental setting.
  arXiv  Detail & Related papers  (2025-06-28T18:36:22Z)
• Tied Prototype Model for Few-Shot Medical Image Segmentation [9.455265838231252]
  We propose a principled reformulation of ADNet with tied prototype locations for foreground and background distributions.<n>Building on its probabilistic foundation, TPM naturally extends to multiple prototypes and multi-class segmentation.<n>We leverage naturally occurring class priors to define an ideal target for adaptive thresholds.
  arXiv  Detail & Related papers  (2025-06-27T10:33:55Z)
• Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation [20.770556378026768]
  Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning.<n>We propose a prototype contrastive learning method called Prototype Contrastive Consistency (PCCS) for semi-supervised medical image segmentation.<n>PCCS achieves better segmentation performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-10T16:40:26Z)
• Mind the Gap Between Prototypes and Images in Cross-domain Finetuning [64.97317635355124]
  We propose a contrastive prototype-image adaptation (CoPA) to adapt different transformations respectively for prototypes and images. Experiments on Meta-Dataset demonstrate that CoPA achieves the state-of-the-art performance more efficiently.
  arXiv  Detail & Related papers  (2024-10-16T11:42:11Z)
• Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images [12.365801596593936]
  Medical image segmentation is one of the domains where sufficient annotated data is not available. We propose a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels. We show that the proposed simple but potent framework performs at par with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-12T15:38:51Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts [9.298299024529333]
  We introduce ProtoSeg, a novel model for interpretable semantic image segmentation.<n>To achieve accuracy comparable to baseline methods, we adapt the mechanism of prototypical parts.<n>We show that ProtoSeg discovers semantic concepts, in contrast to standard segmentation models.
  arXiv  Detail & Related papers  (2023-01-28T19:14:32Z)
• Part-aware Prototype Network for Few-shot Semantic Segmentation [50.581647306020095]
  We propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes.
  arXiv  Detail & Related papers  (2020-07-13T11:03:09Z)

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.