Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.21897v1
• Date: Wed, 28 May 2025 02:22:05 GMT
• Title: Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation
• Authors: Jianchao Jiang, Haofeng Zhang,
• Abstract summary: Few-Shot Medical Image (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images.<n>We propose to focus more attention to those weaker features that are crucial for clear segmentation boundary.
• Score: 17.638595740284636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-based FSMIS methods generate multiple prototypes from the support image solely by random sampling or local averaging, which can cause particularly severe boundary blurring due to the tendency for normal features accounting for the majority of features of a specific category. Consequently, we propose to focus more attention to those weaker features that are crucial for clear segmentation boundary. Specifically, we design a Support Self-Prediction (SSP) module to identify such weak features by comparing true support mask with one predicted by global support prototype. Then, a Hard Prototypes Generation (HPG) module is employed to generate multiple hard prototypes based on these weak features. Subsequently, a Multiple Similarity Maps Fusion (MSMF) module is devised to generate final segmenting mask in a dual-path fashion to mitigate the imbalance between foreground and background in medical images. Furthermore, we introduce a boundary loss to further constraint the edge of segmentation. Extensive experiments on three publicly available medical image datasets demonstrate that our method achieves state-of-the-art performance. Code is available at https://github.com/jcjiang99/CoW.

Related papers

• 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)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors.<n>Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images.<n>Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation [49.5901368256326]
  We propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) in segmenting medical images. Our DAPSAM achieves state-of-the-art performance on two medical image segmentation tasks with different modalities.
  arXiv  Detail & Related papers  (2024-09-19T07:28:33Z)
• 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)
• Few-Shot Medical Image Segmentation with Large Kernel Attention [5.630842216128902]
  We propose a few-shot medical segmentation model that acquire comprehensive feature representation capabilities. Our model comprises four key modules: a dual-path feature extractor, an attention module, an adaptive prototype prediction module, and a multi-scale prediction fusion module. The results demonstrate that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-07-27T02:28:30Z)
• Few-Shot Medical Image Segmentation with High-Fidelity Prototypes [38.073371773707514]
  We propose a novel Detail Self-refined Prototype Network (DSPNet) to construct high-fidelity prototypes representing the object foreground and the background more comprehensively. To construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner.
  arXiv  Detail & Related papers  (2024-06-26T05:06:14Z)
• Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention [1.1155836879100416]
  We propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation. MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features. E-SDM mitigates information loss in multi-task learning with deep supervision.
  arXiv  Detail & Related papers  (2024-05-10T07:34:36Z)
• Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation [23.926487942901872]
  Few-shot Medical Image (FSMIS) is a more promising solution for medical image segmentation tasks. We present an approach to extract multiple representative sub-regions from a given support medical image. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism.
  arXiv  Detail & Related papers  (2023-09-20T09:31:57Z)
• Progressively Dual Prior Guided Few-shot Semantic Segmentation [57.37506990980975]
  Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples. We propose a progressively dual prior guided few-shot semantic segmentation network.
  arXiv  Detail & Related papers  (2022-11-20T16:19:47Z)
• Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation [63.910211095033596]
  Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. We propose a simple yet versatile framework in the spirit of divide-and-conquer. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
  arXiv  Detail & Related papers  (2022-04-21T06:21:14Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Prototype Mixture Models for Few-shot Semantic Segmentation [50.866870384596446]
  Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. We propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82% with only a moderate cost for model size and inference speed.
  arXiv  Detail & Related papers  (2020-08-10T04:33:17Z)

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.