Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound

• URL: http://arxiv.org/abs/2506.23108v1
• Date: Sun, 29 Jun 2025 06:20:15 GMT
• Title: Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound
• Authors: Zhiyuan Zhu, Jian Wang, Yong Jiang, Tong Han, Yuhao Huang, Ang Zhang, Kaiwen Yang, Mingyuan Luo, Zhe Liu, Yaofei Duan, Dong Ni, Tianhong Tang, Xin Yang,
• Abstract summary: We propose a novel Corpus-View-Category Refinement Framework (CVC-RF)<n>CVC-RF processes information from Corpus-, View-, and Category-levels, enhancing model performance.<n> Experimental results indicate that CVC-RF effectively models global features via multi-level refinement.
• Score: 29.02957425057645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-View-Category Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel center-memory contrastive loss, which enhances the network's global modeling capability by comparing with representative cluster centers and diverse negative samples at the Corpus level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at the View level. Finally, a parameter-free mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at the Category level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task.

Related papers

• Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography [0.0]
  This study proposes an automated deep learning system for robust binary classification of breast density.<n>We implemented and compared four advanced convolutional neural networks.<n>We developed a novel Combined Focal Label Smoothing Loss function that integrates focal loss, label smoothing, and class-balanced weighting.
  arXiv  Detail & Related papers  (2025-07-08T21:26:33Z)
• HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation [2.964206587462833]
  A novel semi-supervised segmentation framework, called HDC, is proposed incorporating adaptive consistency learning with a single-teacher architecture.<n>The framework introduces a hierarchical distillation mechanism with two objectives: Correlation Guidance Loss for aligning feature representations and Mutual Information Loss for stabilizing noisy student learning.
  arXiv  Detail & Related papers  (2025-04-14T04:52:24Z)
• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification.<n>We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings.<n>A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification [31.539129126161978]
  This paper introduces a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SCAN is the observation that skin disease images within a class often exhibit multiple sub-clusters. We evaluate the proposed approach on two public datasets for few-shot skin disease classification.
  arXiv  Detail & Related papers  (2022-07-03T16:06:04Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning [20.940530194934972]
  Pyramidal Deep-Broad Learning (PDBL) is a lightweight plug-and-play module for any well-trained classification backbone. PDBL can steadily improve the tissue-level classification performance for any CNN backbones.
  arXiv  Detail & Related papers  (2021-11-04T09:35:12Z)
• PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification [23.466331358975044]
  We study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin stained histopathology data. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types.
  arXiv  Detail & Related papers  (2021-11-01T08:29:40Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification [75.27973258196934]
  We propose a novel Categorical Relation-preserving Contrastive Knowledge Distillation (CRCKD) algorithm, which takes the commonly used mean-teacher model as the supervisor. With this regularization, the feature distribution of the student model shows higher intra-class similarity and inter-class variance. With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively.
  arXiv  Detail & Related papers  (2021-07-07T13:56:38Z)
• 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)

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.