Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

• URL: http://arxiv.org/abs/2309.04825v1
• Date: Sat, 9 Sep 2023 15:39:38 GMT
• Title: Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer
• Authors: Yazhou Zhu, Shidong Wang, Tong Xin, Haofeng Zhang
• Abstract summary: Region-enhanced Prototypical Transformer (RPT) is a few-shot learning-based method to mitigate the effects of large intra-class diversity/bias. By stacking BaT blocks, the proposed RPT can iteratively optimize generated regional prototypes and finally produce rectified and more accurate global prototypes.
• Score: 20.115149216170327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intra-class diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. The source code is available at: https://github.com/YazhouZhu19/RPT.

Related papers

• 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)
• Test-Time Generative Augmentation for Medical Image Segmentation [20.12850876150199]
  We propose a novel approach to enhance medical image segmentation during test time. Instead of employing hand-crafted transforms or functions on the input test image, we advocate for the utilization of an advanced domain-fine-tuned generative model (GM) Experiments conducted across three medical image segmentation tasks demonstrate the efficacy and versatility of the proposed TTGA.
  arXiv  Detail & Related papers  (2024-06-25T14:53:01Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Plug-and-Play Feature Generation for Few-Shot Medical Image Classification [23.969183389866686]
  Few-shot learning presents immense potential in enhancing model generalization and practicality for medical image classification with limited training data. We propose MedMFG, a flexible and lightweight plug-and-play method designed to generate sufficient class-distinctive features from limited samples.
  arXiv  Detail & Related papers  (2023-10-14T02:36:14Z)
• 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)
• 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)
• HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation [5.51045524851432]
  We propose a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model for medical image segmentation. The main purpose is to solve the problem of loss of structural information caused by patch segmentation using transformer. Experimental results show that our proposed method outperforms state-of-the-art models and can achieve superior performance.
  arXiv  Detail & Related papers  (2023-04-10T14:30:03Z)
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [53.575573940055335]
  We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
  arXiv  Detail & Related papers  (2023-01-19T03:42:36Z)
• SATr: Slice Attention with Transformer for Universal Lesion Detection [39.90420943500884]
  Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. We propose a novel Slice Attention Transformer (SATr) block which can be easily plugged into convolution-based ULD backbones. Experiments with five state-of-the-art methods show that the proposed SATr block can provide an almost free boost to lesion detection accuracy.
  arXiv  Detail & Related papers  (2022-03-13T03:37:27Z)
• 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)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)

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.