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BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/mp.12538openalex-snapshot
Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions
inferredcites →https://doi.org/10.1002/mp.15852openalex-snapshot
A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images
inferredcites →https://doi.org/10.1007/978-3-030-31332-6_4openalex-snapshot
Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning
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Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
inferredcites →https://doi.org/10.1007/s00330-021-08452-1openalex-snapshot
The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study
inferredcites →https://doi.org/10.1007/s00330-022-08558-0openalex-snapshot
Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network
inferredcites →https://doi.org/10.1007/s00404-020-05554-xopenalex-snapshot
Comparison of radial and meander-like breast ultrasound with respect to diagnostic accuracy and examination time
inferredcites →https://doi.org/10.1007/s10489-021-03140-5openalex-snapshot
Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function
inferredcites →https://doi.org/10.1007/s10851-007-0015-8openalex-snapshot
Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation
inferredcites →https://doi.org/10.1007/s11042-020-09337-zopenalex-snapshot
Deep feature extraction and classification of breast ultrasound images
inferredcites →https://doi.org/10.1007/s11063-018-09977-1openalex-snapshot
Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
inferredcites →https://doi.org/10.1007/s42600-021-00137-4openalex-snapshot
U-Net and SegNet performances on lesion segmentation of breast ultrasonography images
inferredcites →https://doi.org/10.1016/j.bbe.2018.10.002openalex-snapshot
Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images
inferredcites →https://doi.org/10.1016/j.clbc.2021.03.006openalex-snapshot
Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis
inferredcites →https://doi.org/10.1016/j.cmpb.2017.10.004openalex-snapshot
A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes
inferredcites →https://doi.org/10.1016/j.cmpb.2019.105173openalex-snapshot
Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification
inferredcites →https://doi.org/10.1016/j.cmpb.2020.105361openalex-snapshot
Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compbiomed.2016.11.005openalex-snapshot
A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography
inferredcites →https://doi.org/10.1016/j.compbiomed.2020.104036openalex-snapshot
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound
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Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
inferredcites →https://doi.org/10.1016/j.dib.2019.104863openalex-snapshot
Dataset of breast ultrasound images
inferredcites →https://doi.org/10.1016/j.eswa.2021.115222openalex-snapshot
Nested cross-validation when selecting classifiers is overzealous for most practical applications
inferredcites →https://doi.org/10.1016/j.eswa.2022.119024openalex-snapshot
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
inferredcites →https://doi.org/10.1016/j.ipm.2009.03.002openalex-snapshot
A systematic analysis of performance measures for classification tasks
inferredcites →https://doi.org/10.1016/j.medengphy.2022.103895openalex-snapshot
Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images
inferredcites →https://doi.org/10.1016/j.neucom.2015.04.121openalex-snapshot
Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography
inferredcites →https://doi.org/10.1016/j.patcog.2014.06.006openalex-snapshot
Improving classification performance of breast lesions on ultrasonography
inferredcites →https://doi.org/10.1016/j.patcog.2018.01.032openalex-snapshot
Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities
inferredcites →https://doi.org/10.1016/j.patcog.2018.02.012openalex-snapshot
Automatic breast ultrasound image segmentation: A survey
inferredcites →https://doi.org/10.1016/j.patcog.2021.108427openalex-snapshot
Segmentation information with attention integration for classification of breast tumor in ultrasound image
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2014.06.005openalex-snapshot
Breast Ultrasound Despeckling Using Anisotropic Diffusion Guided by Texture Descriptors
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2016.02.016openalex-snapshot
New Fully Automated Method for Segmentation of Breast Lesions on Ultrasound Based on Texture Analysis
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2019.03.018openalex-snapshot
Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound
inferredcites →https://doi.org/10.1016/s0929-6441(08)60005-3openalex-snapshot
Computer-aided Diagnosis in Breast Ultrasound
inferredcites →https://doi.org/10.1023/a:1010920819831openalex-snapshot
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
inferredcites →https://doi.org/10.1038/s41597-023-02100-7openalex-snapshot
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
inferredcites →https://doi.org/10.1038/s41598-022-18747-yopenalex-snapshot
Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement
inferredcites →https://doi.org/10.1038/sdata.2017.177openalex-snapshot
A curated mammography data set for use in computer-aided detection and diagnosis research
inferredcites →https://doi.org/10.1049/iet-cvi.2016.0244openalex-snapshot
Mammographic mass classification according to Bi‐RADS lexicon
inferredcites →https://doi.org/10.1101/2022.03.07.22272009openalex-snapshot
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
inferredcites →https://doi.org/10.1109/access.2020.3010863openalex-snapshot
Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/jbhi.2017.2731873openalex-snapshot
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tmi.2012.2206398openalex-snapshot
Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound
inferredcites →https://doi.org/10.1109/tpami.2006.79openalex-snapshot
One-shot learning of object categories
inferredcites →https://doi.org/10.1118/1.3265959openalex-snapshot
Computerized lesion segmentation of breast ultrasound based on marker‐controlled watershed transformation
inferredcites →https://doi.org/10.1145/3418355openalex-snapshot
Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images
inferredcites →https://doi.org/10.1162/089976698300017197openalex-snapshot
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
inferredcites →https://doi.org/10.1186/s12880-019-0349-xopenalex-snapshot
An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures
inferredcites →https://doi.org/10.1186/s40537-019-0197-0openalex-snapshot
A survey on Image Data Augmentation for Deep Learning
inferredcites →https://doi.org/10.1186/s40537-019-0276-2openalex-snapshot
Deep convolutional neural network based medical image classification for disease diagnosis
inferredcites →https://doi.org/10.2139/ssrn.4339660openalex-snapshot
A Benchmark for Breast Ultrasound Image Classification
inferredcites →https://doi.org/10.2214/ajr.10.6049openalex-snapshot
The Relationship of Mammographic Density and Age: Implications for Breast Cancer Screening
inferredcites →https://doi.org/10.2214/ajr.13.12072openalex-snapshot
Screening Breast Ultrasound: Past, Present, and Future
inferredcites →https://doi.org/10.2214/ajr.18.20392openalex-snapshot
New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
inferredcites →https://doi.org/10.3322/caac.21660openalex-snapshot
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
inferredcites →https://doi.org/10.3389/fonc.2021.600557openalex-snapshot
Artificial Intelligence in Medical Imaging of the Breast
inferredcites →https://doi.org/10.3390/healthcare10040729openalex-snapshot
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
inferredcites →https://doi.org/10.3390/s20236838openalex-snapshot
Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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SPICE : Leveraging Soft Probabilistic Causal Intervention for Breast Ultrasound Tumor Segmentation
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Prototype bank‐driven test‐time adaptation for medical ultrasound image segmentation
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Hard Sample Analysis of Breast Ultrasound Dataset for Tumour Classification
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SAMUSA: Segment Anything Model 2 for UltraSound Annotation
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ADAptation: Reconstruction-Based Unsupervised Active Learning for Breast Ultrasound Diagnosis
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Training Set Diversity: A Key Factor in AI-Driven Breast Ultrasound Classification
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FedBC: Privacy-Preserving Breast Cancer Diagnosis from Ultrasound Images Using Federated Learning
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DEnPL: Improved Classification in Imbalanced Medical Datasets via Data-Engineered Prototypical Metric Loss
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Simulating Federated Learning for Enhanced Breast Cancer Detection in Ultrasound Images
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Comparison of Deep Learning Architectures for Automated Breast Ultrasound Report Generation
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FedVendorBC: Accurate Multi-vendor Federated Learning for Privacy-Preserving and Generalizable Breast Cancer Diagnosis Using Ultrasound Images
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When to Adapt? Adapting the Model or Data in Federated Medical Imaging
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Towards Unified Image–Video Modeling Across B-Mode and Contrast-Enhanced Ultrasound for Multi-task Analysis
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High-Frequency Guided Feature Refinement for Semi-supervised Ultrasound Image Segmentation
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Organ-Invariant Tumor Representation Learning via Disentangled Adversarial MAE for Multi-organ Ultrasound Imaging
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HAR-MoE: Hierarchical Affinity-Aware Routing Mixture-of-Experts for Unified Multi-organ Ultrasound Analysis
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NMTNet: A Multi-task Deep Learning Network for Joint Segmentation and Classification of Breast Tumors
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Auxiliary-Data Governance in Breast Ultrasound Segmentation: A Multi-cohort Audit of Annotation Compatibility and Negative Transfer
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Invariant Texture Features to Gray Level Discretization
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An enhanced genetic programming algorithm with new genetic operators for medical image classification
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Early Detection and Personalized Risk Assessment of Breast Cancer: An Integrative Review and Comparative Evaluation of AI Models Using Clinical and Imaging Data
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Multi-Regional Feature Learning in AI-Based Breast Cancer Analysis: Overview, Emerging Trends, and Future Perspectives
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CS-TransED: An Optimized Hybrid CNN-Transformer with Channel-Spatial Attention for Computationally Efficient and High-Accuracy Multimodal Medical Image Segmentation
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Swarm-driven fine-tuned hybrid transfer learningarchitecture on breast ultrasound scans for advanced prognostic
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Speckle aware attention and multi scale pooling for robust breast ultrasound lesion classification across multiple benchmarks
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Constructing GLCM-based texture feature expressions using genetic programming for breast tumor classification
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HCMUNet: A hybrid CNN and Mamba network for medical ultrasound image segmentation
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SwinEff-AttentionNet: a dual hybrid model for breast image segmentation and classification using multiple ultrasound modality
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ASFCP-Net: Axial Spatial-Frequency Domain Collaborative Perception Dual-Branch Network for breast lesion segmentation in ultrasound images
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Wavelet-transformer hybrid network for robust breast ultrasound image segmentation with multi-scale feature fusion and global context modeling
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Applications of a novel deep neural network to the classification of liver steatosis and breast lesions in ultrasound images
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ANNet: Adaptive nonlinear network for medical image segmentation with brightness balancing and noise suppression
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Multi-task learning with orthogonal feature disentanglement for breast tumor segmentation and classification in ultrasound images
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USF-MAE: Ultrasound self-supervised foundation model with masked autoencoding
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Medical image segmentation via Dynamic Feature Synergy, deformable semantic alignment and context perception modulation
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Wavelet-ASPP U-Net: Enhancing ultrasound image segmentation with multi-scale context and frequency-aware features
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FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity
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USE-MiT: Attention-based model for breast ultrasound images segmentation
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CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning
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MSDAHNet: A multi-scale dual attention hybrid convolution network for breast tumor segmentation
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A modified MultiResUNet model with attention focus for breast cancer detection
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ESAM2-BLS: Enhanced segment anything model 2 for efficient breast lesion segmentation in ultrasound imaging
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Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis
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HTBE-Net: A hybrid transformer network based on boundary enhancement for breast ultrasound image segmentation
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High-level medical semantic adaptation via an intelligent communication mixture-of-experts for medical image segmentation
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MSRA-Net: A multi-scale and region-aware network for breast cancer ultrasound image segmentation
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HMU-Net: Hybrid multi-scale attention U-Net for breast lesion segmentation in ultrasound
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A dual-domain enhanced network with multi-scale structure guide for breast lesion segmentation in ultrasound images
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EH-former: Regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images
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FABRF-Net: A frequency-aware boundary and region fusion network for breast ultrasound image segmentation
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Interpretable breast cancer diagnosis using histopathology and lesion mask as domain concepts conditional simulation ultrasonography
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URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis
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Deblurring masked image modeling for ultrasound image analysis
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An orchestration learning framework for ultrasound imaging: Prompt-Guided Hyper-Perception and Attention-Matching Downstream Synchronization
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Category-specific unlabeled data risk minimization for ultrasound semi-supervised segmentation
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A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis
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S2DENet: Shallow suppression and deep enhancement network for general ultrasound image segmentation
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SFLFEM: A frequency-enhanced mamba framework for selective personalized federated breast ultrasound diagnosis
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Graph adiabatic diffusion neural networks for distribution-shift breast tumor image classification
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D3-KD: Breast cancer segmentation via data diffusion–enhanced knowledge distillation under source-free domain conditions
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SDC-Net: Semi-supervised breast ultrasound lesion segmentation via semantic decoupling
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Diagnostic Model of Breast Ultrasound Elastography and B-Mode Image Fusion Based on Deep Learning
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BoostCNN: Deep Learning AdaBoost-based Method for Easy and Difficult Nodule Classification in Ultrasound Images
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MSCT-Trans: A Multi-scale Convolutional Neural Network Token Transformer for Interpretable Ultrasound Image Classification
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Addressing data heterogeneity in distributed medical imaging with heterosync learning
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A foundation generative model for breast ultrasound image analysis
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A deep learning system for non-invasive breast cancer diagnosis with multimodal data
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An explainable biomedical foundation model via large-scale concept-enhanced vision–language pretraining
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SwiftMSeg: lightweight multi-scale local–global context modeling with transformer for medical image segmentation
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From pretraining to privacy: federated ultrasound foundation model with self-supervised learning
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On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models
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Optimized multi-attention UNet for breast cancer segmentation using depthwise separable convolutions
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BD-StableNet: a deep stable learning model with an automatic lesion area detection function for predicting malignancy in BI-RADS category 3–4A lesions
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Deep Learning for Breast Lesion Classification on Ultrasound Images: A Novel Multi-Dataset Approach
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MDVM-UNet:an improved multiscale-convolutional dual-pooling vision mamba model for medical ultrasound image segmentation
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification
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D-DDPM: Deep Denoising Diffusion Probabilistic Models for Lesion Segmentation and Data Generation in Ultrasound Imaging
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MBCE: Multi-Band Contrastive Encoding for Frequency-Aware Ultrasound Representation Learning
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Swin-DeepLabV3: Enhanced Semantic Segmentation Through Global-Local Feature Fusion Using Swin Transformer and Atrous Spatial Pyramid Pooling
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Speckle Noise Reduction in Breast Ultrasound Imaging Using a U-Net-Based Generative Adversarial Network
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Data Constraints? Not anymore: Image-Only Learning with Cross-Institutional Applicability for Ultrasound Video Segmentation
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UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation
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UA-MAE: An Uncertainty-Aware Masked Autoencoder for Breast Lesion Segmentation in Ultrasound Images
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MMSeg: A Masked Autoencoder-based Multi-Center Collaborative Framework for Medical Image Segmentation
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DM-UNet: a Dilated Multi-Scale and Bottleneck Attention UNet for Ultrasound Image Segmentation
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Segmentation-Guided Multi-Stream Classification of Breast Ultrasound Images Using Deep Learning
inferred← citeshttps://doi.org/10.1109/cvpr52734.2025.02660openalex-snapshot
Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Images
inferred← citeshttps://doi.org/10.1109/cvprf71909.2026.00537openalex-snapshot
Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning
inferred← citeshttps://doi.org/10.1109/dipca70202.2026.11565852openalex-snapshot
OrdConLA: Ordinal Contrastive Learning with Logit Adjustment for Multi-Center Breast Ultrasound BI-RADS Grading
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11253082openalex-snapshot
TRAM-UNet: Transformer and Region Attention Module based U-Net for Breast Ultrasound Image Segmentation
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11254475openalex-snapshot
WAveHCT: Wavelet-Attentive Hybrid Convolution-Transformer Network for Breast Cancer Diagnosis in Ultrasound Images
inferred← citeshttps://doi.org/10.1109/gaiis69281.2026.11519126openalex-snapshot
OAF-CLIP: Ordinal Attention Fusion for Fine-Grained Prior-Guided Breast Ultrasound Benign-Malignant Classification
inferred← citeshttps://doi.org/10.1109/gcon69192.2026.11648673openalex-snapshot
DWX-Net: A Daubechies Wavelet-enhanced Xception Network for Breast Ultrasound Lesion Classification
inferred← citeshttps://doi.org/10.1109/gmepe/pahce62423.2024.10534649openalex-snapshot
Genetic Programming for Feature Construction in Breast Ultrasound Tumor Classification
inferred← citeshttps://doi.org/10.1109/gmepe/pahce65777.2025.11002817openalex-snapshot
A Novel Curriculum Sampling Method for Hard Observations Classification in Breast Cancer Ultrasound
inferred← citeshttps://doi.org/10.1109/gmepe/pahce69420.2026.11550338openalex-snapshot
BI-RADS Classification of Breast Tumors in Ultrasound Using Convolutional Autoencoder-Based Feature Extractor
inferred← citeshttps://doi.org/10.1109/icaiia70172.2026.11691797openalex-snapshot
Uncertainty-Calibrated Test-Time Adaptation for Cross-Dataset Breast Ultrasound Segmentation
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11460659openalex-snapshot
Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis
inferred← citeshttps://doi.org/10.1109/iccv51701.2025.01993openalex-snapshot
COME: Dual Structure-Semantic Learning with Collaborative MOE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets
inferred← citeshttps://doi.org/10.1109/iccv51701.2025.02014openalex-snapshot
RA-BUSSeg: Relation-Aware Semi-Supervised Breast Ultrasound Image Segmentation via Adjacent Propagation and Cross-Layer Alignment
inferred← citeshttps://doi.org/10.1109/iccvdm66874.2025.11290557openalex-snapshot
A Fully Transformer-Based Multimodal Framework for Explainable Breast Cancer Image Segmentation Using Radiology Reports
inferred← citeshttps://doi.org/10.1109/iccvw69036.2025.00108openalex-snapshot
Breast Cancer Detection with Topological Deep Learning
inferred← citeshttps://doi.org/10.1109/iccvw69036.2025.00763openalex-snapshot
Fairness in Breast Cancer Diagnosis with Deep Learning
inferred← citeshttps://doi.org/10.1109/icipcn67432.2026.11439016openalex-snapshot
Statistically Validated an Explainable Transfer Learning Model for Accurate Breast Cancer Classification in Ultrasound Imaging
inferred← citeshttps://doi.org/10.1109/icm66518.2025.11321319openalex-snapshot
Comparative Study of Deep Learning Models for Multi-Task Breast Cancer Detection in Ultrasound Imaging
inferred← citeshttps://doi.org/10.1109/icme59968.2025.11208978openalex-snapshot
BI-RADS Boosted Breast Cancer Diagnosis With Masked Pretraining On Imbalanced Ultrasound Data
inferred← citeshttps://doi.org/10.1109/indicon68490.2025.11392912openalex-snapshot
FuseMix-BEAM: A Stage-Enhanced Feature Fusion Framework for Breast Ultrasound Image Segmentation
inferred← citeshttps://doi.org/10.1109/isbi60581.2025.10980764openalex-snapshot
Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation
inferred← citeshttps://doi.org/10.1109/isbi60581.2025.10980950openalex-snapshot
Development of an Anatomically Realistic Ultrasound Elastography Radiofrequency Database
inferred← citeshttps://doi.org/10.1109/isbi60581.2025.10980993openalex-snapshot
Visual Prompting and Adaptation of Vision Language Models for Tumor Classification in Breast Ultrasound
inferred← citeshttps://doi.org/10.1109/isbi61048.2026.11515944openalex-snapshot
A Multi-Modal Transformer for Ultrasound-Guided Diffuse Optical Tomography
inferred← citeshttps://doi.org/10.1109/jbhi.2025.3622934openalex-snapshot
Federated Pseudo-Labeling: A Data-Centric, Privacy-Preserving Framework for Medical Image Segmentation
inferred← citeshttps://doi.org/10.1109/meco70748.2026.11579117openalex-snapshot
AGU-Net: An Attention and Gating-aided U-Net Model for Breast Ultrasound Lesion Segmentation
inferred← citeshttps://doi.org/10.1109/ncc68160.2026.11478823openalex-snapshot
SE-CSAF-BUS-Net: Stage-Enhanced Cross-Scale Adaptive Fusion Network for Breast Ultrasound Segmentation
inferred← citeshttps://doi.org/10.1109/ncc68160.2026.11479123openalex-snapshot
HMSF-BUS Net: A Hierarchical Multiscale Fusion Framework for Breast Ultrasound Image Segmentation
inferred← citeshttps://doi.org/10.1109/pais69842.2026.11605242openalex-snapshot
Multi-Center Generalization in Breast Ultrasound Deep Learning: Patient-Level Data Leakage and Cross-Dataset Evaluation
inferred← citeshttps://doi.org/10.1109/ssd64182.2025.10989948openalex-snapshot
Medical Ultrasound Image Improvement Based on Denoising Convolutional Autoencoder
inferred← citeshttps://doi.org/10.1109/ssd64182.2025.10989969openalex-snapshot
Enhancing Breast Ultrasound Diagnostics Using GANs and Guided Filtering
inferred← citeshttps://doi.org/10.1109/ssd64182.2025.10989988openalex-snapshot
Ultrasound Imaging Enhancement Using Denoising AutoEncoders
inferred← citeshttps://doi.org/10.1109/tim.2025.3566814openalex-snapshot
MulFF-Net: A Domain-Aware Multiscale Feature Fusion Network for Breast Ultrasound Image Segmentation With Radiomic Applications
inferred← citeshttps://doi.org/10.1109/tuffc.2025.3566340openalex-snapshot
Synthetic Versus Classic Data Augmentation: Impacts on Breast Ultrasound Image Classification
inferred← citeshttps://doi.org/10.1109/tuffc.2025.3605285openalex-snapshot
Grounding DINO-US-SAM: Text-Prompted Multiorgan Segmentation in Ultrasound With LoRA-Tuned Vision–Language Models
inferred← citeshttps://doi.org/10.1155/acis/1913589openalex-snapshot
Deep Learning in Medical Imaging: Chronological Evolution, Frameworks, Core Methods, and Recent Advances in Breast Cancer Segmentation (2023–2024)
inferred← citeshttps://doi.org/10.1177/01617346251346060openalex-snapshot
MVKD-Trans: A Multi-View Knowledge Distillation Vision Transformer Architecture for Breast Cancer Classification Based on Ultrasound Images
inferred← citeshttps://doi.org/10.1177/01617346251384596openalex-snapshot
Novel Clinical Hybrid Deep Framework for Denoising and Anatomical Segmentation in Challenging Ultrasound Conditions
inferred← citeshttps://doi.org/10.1177/01617346251388454openalex-snapshot
DAUS-Net: Toward Ultrasound Scanner-Agnostic Domain Generalized Robust and Accurate Segmentation
inferred← citeshttps://doi.org/10.1177/01617346261452679openalex-snapshot
Hybrid Physics-Driven Deep Learning for Enhanced Ultrasound Image Quality and Speckle Noise Suppression
inferred← citeshttps://doi.org/10.1177/01617346261463796openalex-snapshot
LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images
inferred← citeshttps://doi.org/10.1615/critrevbiomedeng.2025058842openalex-snapshot
Innovative Ultrasound Image Denoising Using Channel Attention and Variational Autoencoders
inferred← citeshttps://doi.org/10.1615/critrevbiomedeng.2026060806openalex-snapshot
Denoising of Medical Ultrasound Images Using Variational Autoencoders Guided by Attention Mechanisms
inferred← citeshttps://doi.org/10.17798/bitlisfen.1587411openalex-snapshot
Breast Cancer Classification in Ultrasound Imaging Using Cost-Sensitive Learning and K-Means SMOTE on the Imbalanced BUSI Dataset with Deep Feature Extraction
inferred← citeshttps://doi.org/10.2139/ssrn.6330508openalex-snapshot
R2C-Net: Reliability-Aware and Consistency-Guided Co-Training Network for Semi-Supervised Ultrasound Segmentation
inferred← citeshttps://doi.org/10.24132/csrn.2025-5openalex-snapshot
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation
inferred← citeshttps://doi.org/10.26437/r9mces28openalex-snapshot
Development of an Explainable Deep Learning Model Based on Morphological Component Analysis (MCA) for Breast Cancer Detection in Ultrasound Images
inferred← citeshttps://doi.org/10.32604/cmc.2024.058488openalex-snapshot
Advancing Breast Cancer Diagnosis: The Development and Validation of the HERA-Net Model for Thermographic Analysis
inferred← citeshttps://doi.org/10.3389/fonc.2025.1672274openalex-snapshot
Research on breast tumor segmentation based on the Mamba architecture
inferred← citeshttps://doi.org/10.3389/fonc.2026.1857871openalex-snapshot
Multi-scale information bottleneck with confidence-weighted decision fusion for robust breast ultrasound lesion classification
inferred← citeshttps://doi.org/10.3389/fphys.2024.1432987openalex-snapshot
A dual-branch and dual attention transformer and CNN hybrid network for ultrasound image segmentation
inferred← citeshttps://doi.org/10.3389/frai.2026.1934912openalex-snapshot
Frequency response compensation to optimize pretrained deep learning models in lung nodule malignancy classification
inferred← citeshttps://doi.org/10.3390/aimed1030018openalex-snapshot
Scoping Review of Recent Trends and Challenges in Artificial Intelligence Based Medical Ultrasound Denoising
inferred← citeshttps://doi.org/10.3390/bioengineering12101110openalex-snapshot
Review of Artificial Intelligence Techniques for Breast Cancer Detection with Different Modalities: Mammography, Ultrasound, and Thermography Images
inferred← citeshttps://doi.org/10.3390/biomedinformatics5030046openalex-snapshot
Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches
inferred← citeshttps://doi.org/10.3390/biomedinformatics6050074openalex-snapshot
Knowing When to Defer: Trustworthy Multimodal AI for BI-RADS-Derived Management Using Paired Mammography and Ultrasound
inferred← citeshttps://doi.org/10.3390/diagnostics14080848openalex-snapshot
Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024
inferred← citeshttps://doi.org/10.3390/diagnostics16040622openalex-snapshot
An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images
inferred← citeshttps://doi.org/10.3390/diagnostics16081203openalex-snapshot
CMT-BUSNet: Adaptive Fusion-Based Triple-Branch Hybrid Architecture for Explainable Breast Ultrasound Tumor Segmentation
inferred← citeshttps://doi.org/10.3390/diagnostics16101537openalex-snapshot
Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark
inferred← citeshttps://doi.org/10.3390/info17100954openalex-snapshot
Operating-Point Collapse Under Normal-Class Source Confounding in Cross-Dataset Breast Ultrasound Classification
inferred← citeshttps://doi.org/10.3390/ndt4030028openalex-snapshot
Cross-Dataset Evaluation of the Lightweight YOLO Family for Breast Ultrasound Lesion Segmentation: Effects of Preprocessing, Hyperparameter Optimization, and Test-Time Augmentation
inferred← citeshttps://doi.org/10.3390/s26030974openalex-snapshot
Robust Dual-Stream Diagnosis Network for Ultrasound Breast Tumor Classification with Cross-Domain Segmentation Priors
inferred← citeshttps://doi.org/10.5753/sbcas.2026.21356openalex-snapshot
Ensemble Ponderado de Deep Learning para Segmentação de Lesões Mamárias em Ultrassonografia
inferred← citeshttps://doi.org/10.5753/sbsi.2026.248598openalex-snapshot
Interactive Information Visualization as Support for Computer-Aided Diagnosis: Prototype and Qualitative Evaluation
inferred← citeshttps://doi.org/10.64898/2026.03.06.26347800openalex-snapshot
Lesion-Centric Latent Phenotypes from Segmentation Encoders for Breast Ultrasound Interpretability
inferred← citeshttps://doi.org/10.7759/cureus.83378openalex-snapshot
CyberKnife Staged Radiotherapy for Elderly Patients With Poorly Differentiated Lung Adenocarcinoma: A Case Report and Dosimetric Analysis
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