Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis

• URL: http://arxiv.org/abs/2605.25518v1
• Date: Mon, 25 May 2026 07:20:13 GMT
• Title: Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis
• Abstract summary: This paper proposes the Cross-Stage Attention Mixture-of-Experts Network (CSA-MoE-Net) to improve feature representation and classification accuracy.<n>It adopts a Cross-Stage Attention-enhanced ResNet-18 as the backbone, in which the Cross-Stage Attention module adaptively recalibrates multi-level features.<n> Experiments on a balanced dataset of 2,129 breast ultrasound images show that, averaged over 20 independent runs, the model achieves an accuracy of 96.33%, precision of 94.09%, recall of 98.53%, F1-score of 96.25%, and
• Score: 6.727825549527392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast ultrasound imaging is an important noninvasive method for early breast cancer diagnosis, but automatic benign/malignant classification remains challenging due to tumor heterogeneity, blurred boundaries, and data imbalance. To improve feature representation and classification accuracy, this paper proposes the Cross-Stage Attention Mixture-of-Experts Network (CSA-MoE-Net). It adopts a Cross-Stage Attention-enhanced ResNet-18 as the backbone, in which the Cross-Stage Attention module adaptively recalibrates multi-level features, thereby enhancing key tumor features and suppressing redundancy. A three-branch Mixture of Experts (MoE) Block learns complementary features from the Whole Tumor Image, Tumor Core, and Boundary, and an Adaptive Gating Network fuses them to capture morphological, textural, and contextual information. The fused features are denoted as Fused Expert Feature (FEF) in the architecture. Experiments on a balanced dataset of 2,129 breast ultrasound images show that, averaged over 20 independent runs, the model achieves an accuracy of 96.33\%, precision of 94.09\%, recall of 98.53\%, F1-score of 96.25\%, and AUC of 99.50\%. Compared to the baseline ResNet-18, these metrics improve by 3.01, 0.70, 5.37, 2.98, and 5.42 percentage points, respectively. The proposed mechanism requires no invasive modification and can be seamlessly embedded into VGG-16, DenseNet-121, etc., yielding stable performance gains, thus providing reliable support for computer-aided diagnosis.

Related papers

• ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization [4.546739588274135]
  Gliomas are among the most aggressive cancers, characterized by high mortality rates and complex diagnostic processes.<n>Existing studies on glioma diagnosis and classification often describe issues such as high variability in imaging data.<n>We propose novel techniques utilizing multi-parametric MRI data to enhance tumor segmentation and classification efficiency.
  arXiv  Detail & Related papers  (2025-12-27T12:12:53Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• MobileDenseAttn:A Dual-Stream Architecture for Accurate and Interpretable Brain Tumor Detection [0.0]
  We introduce MobileDenseAttn, a fusion model of MobileNetV2 and DenseNet201.<n>MobileDenseAttn is trained on an augmented dataset of 6,020 MRI scans representing glioma, meningioma, pituitary tumors, and normal samples.<n>It provides a training accuracy of 99.75%, a testing accuracy of 98.35%, and a stable F1 score of 0.9835.
  arXiv  Detail & Related papers  (2025-08-22T13:24:38Z)
• HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging [2.7205074719266062]
  Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning.<n>We introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net)<n>HANS-Net combines hyperbolic convolutions for hierarchical geometric representation, a wavelet-inspired decomposition module for multi-scale texture learning, and an implicit neural representation branch.
  arXiv  Detail & Related papers  (2025-07-15T13:56:37Z)
• DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation [0.0]
  This paper proposes a novel dual cross-attention fusion model for medical image analysis.<n>It addresses key challenges in feature integration and interpretability.<n>The proposed model achieved AUC of 99.75%, 100%, 99.93% and 98.69% and AUPR of 99.81%, 100%, 99.97%, and 96.36% on Covid-19, Tuberculosis, Pneumonia Chest X-ray images and Retinal OCT images respectively.
  arXiv  Detail & Related papers  (2025-03-14T20:28:20Z)
• Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification [0.0]
  We propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification.<n>We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework.<n>We also introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net)
  arXiv  Detail & Related papers  (2024-07-03T06:40:26Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics [47.187609203210705]
  2D and 3D T$$-weighted images of a pelvic phantom were acquired on three scanners. repeatability and repositioning of radiomic features were assessed.
  arXiv  Detail & Related papers  (2020-05-14T09:24:48Z)

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.