Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging

• URL: http://arxiv.org/abs/2503.18170v1
• Date: Sun, 23 Mar 2025 18:47:12 GMT
• Title: Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging
• Authors: Abderrachid Hamrani, Anuradha Godavarty,
• Abstract summary: This paper introduces the Diffusion Attention Zero-shot Unsupervised System (ADZUS), a novel approach for zero-shot biomedical image segmentation.<n>ADZUS harnesses the intrinsic capabilities of pre-trained diffusion models, utilizing their generative and discriminative potentials to segment medical images.<n> Experimental results across various medical imaging datasets, including skin lesion segmentation, chest X-ray infection segmentation, and white blood cell segmentation, reveal that ADZUS achieves state-of-the-art performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has explored large-scale supervised training to enable segmentation across various medical imaging modalities and unsupervised training to facilitate segmentation without dense annotations. However, constructing a model capable of segmenting diverse medical images in a zero-shot manner without any annotations remains a significant hurdle. This paper introduces the Attention Diffusion Zero-shot Unsupervised System (ADZUS), a novel approach that leverages self-attention diffusion models for zero-shot biomedical image segmentation. ADZUS harnesses the intrinsic capabilities of pre-trained diffusion models, utilizing their generative and discriminative potentials to segment medical images without requiring annotated training data or prior domain-specific knowledge. The ADZUS architecture is detailed, with its integration of self-attention mechanisms that facilitate context-aware and detail-sensitive segmentations being highlighted. Experimental results across various medical imaging datasets, including skin lesion segmentation, chest X-ray infection segmentation, and white blood cell segmentation, reveal that ADZUS achieves state-of-the-art performance. Notably, ADZUS reached Dice scores ranging from 88.7\% to 92.9\% and IoU scores from 66.3\% to 93.3\% across different segmentation tasks, demonstrating significant improvements in handling novel, unseen medical imagery. It is noteworthy that while ADZUS demonstrates high effectiveness, it demands substantial computational resources and extended processing times. The model's efficacy in zero-shot settings underscores its potential to reduce reliance on costly annotations and seamlessly adapt to new medical imaging tasks, thereby expanding the diagnostic capabilities of AI-driven medical imaging technologies.

Related papers

• Dynamically evolving segment anything model with continuous learning for medical image segmentation [50.92344083895528]
  We introduce EvoSAM, a dynamically evolving medical image segmentation model.<n>EvoSAM continuously accumulates new knowledge from an ever-expanding array of scenarios and tasks.<n>Experiments conducted by surgical clinicians on blood vessel segmentation confirm that EvoSAM enhances segmentation efficiency based on user prompts.
  arXiv  Detail & Related papers  (2025-03-08T14:37:52Z)
• MRGen: Segmentation Data Engine For Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically significant imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize training data, to train segmentation models for underrepresented modalities.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• MedUHIP: Towards Human-In-the-Loop Medical Segmentation [5.520419627866446]
  Medical image segmentation is particularly complicated by inherent uncertainties. We propose a novel approach that integrates an textbfuncertainty-aware model with textbfhuman-in-the-loop interaction Our method showcases superior segmentation capabilities, outperforming a wide range of deterministic and uncertainty-aware models.
  arXiv  Detail & Related papers  (2024-08-03T01:06:02Z)
• MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation [2.2585213273821716]
  We propose a novel framework, called MedCLIP-SAM, that combines CLIP and SAM models to generate segmentation of clinical scans. By extensively testing three diverse segmentation tasks and medical image modalities, our proposed framework has demonstrated excellent accuracy.
  arXiv  Detail & Related papers  (2024-03-29T15:59:11Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelines [0.13854111346209866]
  Segment Anything Model (SAM) harnesses a massive training dataset to segment nearly any object. Our findings reveal that SAM's zero-shot performance is not only comparable, but in certain cases, surpasses the current state-of-the-art. We propose practical guidelines that require minimal interaction while consistently yielding robust outcomes.
  arXiv  Detail & Related papers  (2023-04-28T22:07:24Z)
• Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation [5.547422331445511]
  We report quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks. Our study indicates the versatility of generalist vision foundation models on medical imaging.
  arXiv  Detail & Related papers  (2023-04-25T08:07:59Z)
• 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)
• Analysing the effectiveness of a generative model for semi-supervised medical image segmentation [23.898954721893855]
  State-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. Semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Deep generative models such as the SemanticGAN are truly viable alternatives to tackle challenging medical image segmentation problems.
  arXiv  Detail & Related papers  (2022-11-03T15:19:59Z)
• CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation [8.307551496968156]
  We present CUTS, an unsupervised deep learning framework for medical image segmentation. For each image, it produces an embedding map via intra-image contrastive learning and local patch reconstruction. CUTS yields a series of coarse-to-fine-grained segmentations that highlight features at various granularities.
  arXiv  Detail & Related papers  (2022-09-23T01:09:06Z)
• SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation [47.001609080453335]
  We propose a novel Semi-Supervised Medical image Detector (SSMD) The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings.
  arXiv  Detail & Related papers  (2021-06-03T01:59:50Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)

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.