Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation

• URL: http://arxiv.org/abs/2503.12068v1
• Date: Sat, 15 Mar 2025 09:55:31 GMT
• Title: Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
• Authors: Qingchen Tang, Lei Fan, Maurice Pagnucco, Yang Song,
• Abstract summary: Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations.<n>Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions.
• Score: 13.640757848445835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions, leading to incomplete masks. Recent approaches that introduce textual information struggle with histopathological images due to inter-class homogeneity and intra-class heterogeneity. In this paper, we propose a prototype-based image prompting framework for histopathological image segmentation. It constructs an image bank from the training set using clustering, extracting multiple prototype features per class to capture intra-class heterogeneity. By designing a matching loss between input features and class-specific prototypes using contrastive learning, our method addresses inter-class homogeneity and guides the model to generate more accurate CAMs. Experiments on four datasets (LUAD-HistoSeg, BCSS-WSSS, GCSS, and BCSS) show that our method outperforms existing weakly supervised segmentation approaches, setting new benchmarks in histopathological image segmentation.

Related papers

• 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. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Freestyle Sketch-in-the-Loop Image Segmentation [116.1810651297801]
  We introduce a "sketch-in-the-loop" image segmentation framework, enabling the segmentation of visual concepts partially, completely, or in groupings.<n>This framework capitalises on the synergy between sketch-based image retrieval models and large-scale pre-trained models.<n>Our purpose-made augmentation strategy enhances the versatility of our sketch-guided mask generation, allowing segmentation at multiple levels.
  arXiv  Detail & Related papers  (2025-01-27T13:07:51Z)
• Discriminative Image Generation with Diffusion Models for Zero-Shot Learning [53.44301001173801]
  We present DIG-ZSL, a novel Discriminative Image Generation framework for Zero-Shot Learning.<n>We learn a discriminative class token (DCT) for each unseen class under the guidance of a pre-trained category discrimination model (CDM)<n>In this paper, the extensive experiments and visualizations on four datasets show that our DIG-ZSL: (1) generates diverse and high-quality images, (2) outperforms previous state-of-the-art nonhuman-annotated semantic prototype-based methods by a large margin, and (3) achieves comparable or better performance than baselines that leverage human-annot
  arXiv  Detail & Related papers  (2024-12-23T02:18:54Z)
• Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification [3.3104978705632777]
  We propose a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images. The method achieves state-of-the-art performance among methods trained with the same level of supervision. The benefits of the proposed co-segmentation method are further demonstrated in the task of large-scale sketch recognition.
  arXiv  Detail & Related papers  (2024-10-17T14:16:45Z)
• Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation [18.755759024796216]
  Deep neural networks have greatly advanced histopathology image segmentation but usually require abundant data. We present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. We show that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines.
  arXiv  Detail & Related papers  (2022-11-26T06:17:21Z)
• Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation [72.33139350241044]
  Weakly Supervised Semantic COCO (WSSS) based on image-level labels has attracted much attention due to low annotation costs. We propose a Self-supervised Image-specific Prototype Exploration (SIPE) that consists of an Image-specific Prototype Exploration (IPE) and a General-Specific Consistency (GSC) loss. Our SIPE achieves new state-of-the-art performance using only image-level labels.
  arXiv  Detail & Related papers  (2022-03-06T09:01:03Z)
• Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
  We propose a multi-level contrastive learning approach for semantic matching. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
  arXiv  Detail & Related papers  (2021-09-22T18:34:14Z)
• Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation [16.517086214275654]
  We present a novel semi-supervised 2D medical segmentation solution that applies Contrastive Learning (CL) on image patches, instead of full images. These patches are meaningfully constructed using the semantic information of different classes obtained via pseudo labeling. We also propose a novel consistency regularization scheme, which works in synergy with contrastive learning.
  arXiv  Detail & Related papers  (2021-06-12T15:43:24Z)
• Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation [58.96902899546075]
  One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. We propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category.
  arXiv  Detail & Related papers  (2021-02-22T12:07:35Z)
• Deep Active Learning for Joint Classification & Segmentation with Weak Annotator [22.271760669551817]
  CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. We propose an active learning framework, which progressively integrates pixel-level annotations during training. Our results indicate that, by simply using random sample selection, the proposed approach can significantly outperform state-of-the-art CAMs and AL methods.
  arXiv  Detail & Related papers  (2020-10-10T03:25:54Z)
• Attention Model Enhanced Network for Classification of Breast Cancer Image [54.83246945407568]
  AMEN is formulated in a multi-branch fashion with pixel-wised attention model and classification submodular. To focus more on subtle detail information, the sample image is enhanced by the pixel-wised attention map generated from former branch. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed method under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:44:21Z)
• Zero-Shot Recognition through Image-Guided Semantic Classification [9.291055558504588]
  We present a new embedding-based framework for zero-shot learning (ZSL) Motivated by the binary relevance method for multi-label classification, we propose to inversely learn the mapping between an image and a semantic classifier. IGSC is conceptually simple and can be realized by a slight enhancement of an existing deep architecture for classification.
  arXiv  Detail & Related papers  (2020-07-23T06:22:40Z)

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.