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Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

https://doi.org/10.1002/mp.12538
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-94-007-6952-6openalex-snapshot
Quantitative Ultrasound in Soft Tissues
inferredcites →https://doi.org/10.1016/j.bspc.2012.02.002openalex-snapshot
Ultrasound image enhancement: A review
inferredcites →https://doi.org/10.1016/j.crad.2016.11.009openalex-snapshot
Usefulness of combined BI-RADS analysis and Nakagami statistics of ultrasound echoes in the diagnosis of breast lesions
inferredcites →https://doi.org/10.1016/j.patcog.2009.05.012openalex-snapshot
Automated breast cancer detection and classification using ultrasound images: A survey
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.ultrasmedbio.2009.10.006openalex-snapshot
Ultrasonic Nakagami Imaging: A Strategy to Visualize the Scatterer Properties of Benign and Malignant Breast Tumors
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2010.04.001openalex-snapshot
A Critical Review and Uniformized Representation of Statistical Distributions Modeling the Ultrasound Echo Envelope
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2014.11.016openalex-snapshot
Quantitative Sonography of Basal Cell Carcinoma
inferredcites →https://doi.org/10.1088/0031-9155/48/14/313openalex-snapshot
Classification of breast masses in ultrasonic B scans using Nakagami and K distributions
inferredcites →https://doi.org/10.1088/0031-9155/53/21/009openalex-snapshot
Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study
inferredcites →https://doi.org/10.1088/0031-9155/60/19/7513openalex-snapshot
Beamforming effects on generalized Nakagami imaging
inferredcites →https://doi.org/10.1109/58.842062openalex-snapshot
A general statistical model for ultrasonic backscattering from tissues
inferredcites →https://doi.org/10.1109/tuffc.2013.2650openalex-snapshot
A statistical model for the ultrasonic backscattered echo from tissue containing microcalcifications
inferredcites →https://doi.org/10.1118/1.3566064openalex-snapshot
Classification of scattering media within benign and malignant breast tumors based on ultrasound texture‐feature‐based and Nakagami‐parameter images
inferredcites →https://doi.org/10.1118/1.4962928openalex-snapshot
Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters
inferredcites →https://doi.org/10.1148/radiol.14140318openalex-snapshot
The Added Value of Statistical Modeling of Backscatter Properties in the Management of Breast Lesions at US
inferredcites →https://doi.org/10.12968/nuwa.2014.10.2.1142051openalex-snapshot
EYFS Best Practice: All about … children's storymaking
inferredcites →https://doi.org/10.15557/jou.2012.0010openalex-snapshot
Standards of the Polish Ultrasound Society – update. Sonomammography examination

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← backgroundhttps://doi.org/10.5281/zenodo.545928zenodo-depositor-declared
inferred← citeshttps://doi.org/10.1002/adfm.202302929openalex-snapshot
Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications
inferred← citeshttps://doi.org/10.1002/jum.16564openalex-snapshot
Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer
inferred← citeshttps://doi.org/10.1002/jum.16745openalex-snapshot
Ultrasound Radio Frequency Time Series for Tissue Typing
inferred← citeshttps://doi.org/10.1002/mp.13361openalex-snapshot
Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion
inferred← citeshttps://doi.org/10.1002/mp.16287openalex-snapshot
BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets
inferred← citeshttps://doi.org/10.1002/mp.16812openalex-snapshot
BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems
inferred← citeshttps://doi.org/10.1007/978-3-030-31332-6_4openalex-snapshot
Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-12638-3_30openalex-snapshot
Medical Ultrasound Image Segmentation Using U-Net Architecture
inferred← citeshttps://doi.org/10.1007/978-3-031-61628-0_33openalex-snapshot
Texture Analysis of H-scan Ultrasound Images for the Characterization of Breast Tumors
inferred← citeshttps://doi.org/10.1007/978-3-031-62523-7_33openalex-snapshot
Computer-Aided System for Breast Cancer Lesion Segmentation and Classification Using Ultrasound Images
inferred← citeshttps://doi.org/10.1007/978-3-032-05185-1_13openalex-snapshot
E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation
inferred← citeshttps://doi.org/10.1007/978-3-032-20287-1_1openalex-snapshot
Frequency-Dependent Attenuation Compensation in H-scan Ultrasound for Improved Differentiation of Benign and Malignant Breast Tumors
inferred← citeshttps://doi.org/10.1007/978-981-15-4029-5_13openalex-snapshot
Speckle Noise Reduction of In Vivo Ultrasound Image Using Multi-frame Super-Resolution Through Sparse Representation
inferred← citeshttps://doi.org/10.1007/s10278-025-01487-6openalex-snapshot
Breast Ultrasound Image Segmentation Using Multi-branch Skip Connection Search
inferred← citeshttps://doi.org/10.1007/s10278-026-01931-1openalex-snapshot
Deep Learning for Medical Ultrasound Image Segmentation: A Systematic Review of the Current Research
inferred← citeshttps://doi.org/10.1007/s10396-026-01675-zopenalex-snapshot
Dendritic U-Net for robust vascular wall segmentation in ultrasound imaging
inferred← citeshttps://doi.org/10.1007/s10462-022-10152-1openalex-snapshot
Modality specific U-Net variants for biomedical image segmentation: a survey
inferred← citeshttps://doi.org/10.1007/s11042-023-16461-zopenalex-snapshot
Medical ultrasound image segmentation using Multi-Residual U-Net architecture
inferred← citeshttps://doi.org/10.1007/s11517-023-02928-6openalex-snapshot
Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks
inferred← citeshttps://doi.org/10.1007/s11548-023-02849-7openalex-snapshot
A hybrid attentional guidance network for tumors segmentation of breast ultrasound images
inferred← citeshttps://doi.org/10.1007/s11633-022-1382-8openalex-snapshot
Pre-training in Medical Data: A Survey
inferred← citeshttps://doi.org/10.1007/s11831-023-09968-zopenalex-snapshot
A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning
inferred← citeshttps://doi.org/10.1007/s40477-020-00557-5openalex-snapshot
Methods for the segmentation and classification of breast ultrasound images: a review
inferred← citeshttps://doi.org/10.1007/s40846-020-00585-wopenalex-snapshot
Cross-Tissue/Organ Transfer Learning for the Segmentation of Ultrasound Images Using Deep Residual U-Net
inferred← citeshttps://doi.org/10.1016/j.bbe.2018.05.003openalex-snapshot
Discriminant analysis of neural style representations for breast lesion classification in ultrasound
inferred← citeshttps://doi.org/10.1016/j.bbe.2020.04.002openalex-snapshot
Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.bbe.2020.10.007openalex-snapshot
Numerical prediction of breast skin temperature based on thermographic and ultrasonographic data in healthy and cancerous breasts
inferred← citeshttps://doi.org/10.1016/j.bspc.2019.02.020openalex-snapshot
Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification
inferred← citeshttps://doi.org/10.1016/j.bspc.2020.102027openalex-snapshot
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.103559openalex-snapshot
Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104425openalex-snapshot
AMS-PAN: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105705openalex-snapshot
Multimodal image feature fusion for improving medical ultrasound image segmentation
inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106612openalex-snapshot
Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.106884openalex-snapshot
Attention guided neural ODE network for breast tumor segmentation in medical images
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2026.111465openalex-snapshot
Research on breast ultrasound images lesion localization and diagnosis based on knowledge-driven and data-driven methods
inferred← citeshttps://doi.org/10.1016/j.compmedimag.2019.03.001openalex-snapshot
Patch similarity in ultrasound images with hypothesis testing and stochastic distances
inferred← citeshttps://doi.org/10.1016/j.ejmp.2025.104993openalex-snapshot
Comparative analysis of deep learning methods for breast ultrasound lesion detection and classification
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110355openalex-snapshot
DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124835openalex-snapshot
Graph Neural Network Enhanced Dual-Branch Network for lesion segmentation in ultrasound images
inferred← citeshttps://doi.org/10.1016/j.ibmed.2025.100270openalex-snapshot
RETRACTED: (KAUH-BCUSD): Computer-aided breast cancer diagnosis using transformer and CNN using ultrasound dataset
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105522openalex-snapshot
Segmentation-based BI-RADS ensemble classification of breast tumours in ultrasound images
inferred← citeshttps://doi.org/10.1016/j.imavis.2024.105045openalex-snapshot
Optimal fusion of features from decomposed ultrasound RF data with adaptive weighted ensemble classifier to improve breast lesion classification
inferred← citeshttps://doi.org/10.1016/j.imu.2025.101698openalex-snapshot
Recognition of breast cancer from heterogeneous ultrasound images: A multi-level deep learning approach
inferred← citeshttps://doi.org/10.1016/j.jocs.2022.101816openalex-snapshot
Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images
inferred← citeshttps://doi.org/10.1016/j.media.2021.102326openalex-snapshot
W-Net: Dense and diagnostic semantic segmentation of subcutaneous and breast tissue in ultrasound images by incorporating ultrasound RF waveform data
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.04.021openalex-snapshot
ASS-GAN: Asymmetric semi-supervised GAN for breast ultrasound image segmentation
inferred← citeshttps://doi.org/10.1016/j.patcog.2023.109728openalex-snapshot
Rethinking the unpretentious U-net for medical ultrasound image segmentation
inferred← citeshttps://doi.org/10.1016/j.sigpro.2022.108570openalex-snapshot
The multitaper reassigned spectrogram for oscillating transients with Gaussian envelopes
inferred← citeshttps://doi.org/10.1016/j.ultras.2021.106682openalex-snapshot
Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.ultras.2023.107233openalex-snapshot
Quantitative ultrasound parameters from scattering and propagation may reduce the biopsy rate for breast tumor
inferred← citeshttps://doi.org/10.1016/j.ultras.2024.107256openalex-snapshot
Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications
inferred← citeshttps://doi.org/10.1016/j.ultras.2024.107406openalex-snapshot
Spectral analysis enhanced net (SAE-Net) to classify breast lesions with BI-RADS category 4 or higher
inferred← citeshttps://doi.org/10.1016/j.ultras.2024.107498openalex-snapshot
RFImageNet framework for segmentation of ultrasound images with spectra-augmented radiofrequency signals
inferred← citeshttps://doi.org/10.1016/j.ultrasmedbio.2022.11.019openalex-snapshot
A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization
inferred← citeshttps://doi.org/10.1016/j.ultrasmedbio.2023.06.015openalex-snapshot
General Multiscenario Ultrasound Image Tumor Diagnosis Method Based on Unsupervised Domain Adaptation
inferred← citeshttps://doi.org/10.1038/s41467-021-25925-5openalex-snapshot
Time-varying data processing with nonvolatile memristor-based temporal kernel
inferred← citeshttps://doi.org/10.1038/s41597-024-02984-zopenalex-snapshot
Curated benchmark dataset for ultrasound based breast lesion analysis
inferred← citeshttps://doi.org/10.1038/s41597-025-04562-3openalex-snapshot
BUS-UCLM: Breast ultrasound lesion segmentation dataset
inferred← citeshttps://doi.org/10.1038/s41598-024-76308-xopenalex-snapshot
Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour
inferred← citeshttps://doi.org/10.1080/21681163.2024.2302387openalex-snapshot
Machine learning in breast cancer imaging: a review on data, models and methods
inferred← citeshttps://doi.org/10.1080/21681163.2024.2373996openalex-snapshot
A comparative study of breast tumour detection using a semantic segmentation network coupled with different pretrained CNNs
inferred← citeshttps://doi.org/10.1088/1757-899x/1105/1/012079openalex-snapshot
Breast Tissue Characterization Based on Generalised Extreme Value Distribution of Ultrasound Radio-Frequency Data
inferred← citeshttps://doi.org/10.1101/2025.09.14.25335722openalex-snapshot
Deep Learning for Breast Mass Discrimination: Integration of B-Mode Ultrasound & Nakagami Imaging with Automatic Lesion Segmentation
inferred← citeshttps://doi.org/10.1109/access.2024.3442374openalex-snapshot
Ultrasound Breast Image Classification Through Domain Knowledge Integration Into Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/access.2025.3585377openalex-snapshot
Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data
inferred← citeshttps://doi.org/10.1109/ccssta69471.2026.11635467openalex-snapshot
Bayesian Adapter-Enhanced SAM-Med2D for Box-Prompted Ultrasound Image Segmentation
inferred← citeshttps://doi.org/10.1109/dac18072.2020.9218570openalex-snapshot
Robust Design of Large Area Flexible Electronics via Compressed Sensing
inferred← citeshttps://doi.org/10.1109/eit48999.2020.9208264openalex-snapshot
Faster Region Convolutional Neural Networks Applied to Ultrasonic Images for Breast Lesion Detection and 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/icaii59460.2023.10497291openalex-snapshot
A Unified Segmentation Network for Multi-Organ Lesion Detection in Two-Dimensional Grayscale Ultrasound Images
inferred← citeshttps://doi.org/10.1109/icivc58118.2023.10269830openalex-snapshot
Multimodal U-Net Breast Cancer Tumor Algorithm Based on Radio Frequency (RF) Data and Ultrasound Images
inferred← citeshttps://doi.org/10.1109/ieeeconf58974.2023.10405346openalex-snapshot
Developing a Computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging
inferred← citeshttps://doi.org/10.1109/incacct65424.2025.11011293openalex-snapshot
Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning-Based Classification and Segmentation Techniques Using Ultrasound Imaging
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/ius51837.2023.10306634openalex-snapshot
Multimodal Deep Learning Approaches to Breast Tumor Characterization using Ultrasound B-Mode and Nakagami Parametric Images
inferred← citeshttps://doi.org/10.1109/ius51837.2023.10308082openalex-snapshot
Ultrasonic Spectral Information Promotes Specificity and Visualization of Breast Cancer in Deep Learning
inferred← citeshttps://doi.org/10.1109/ius52206.2021.9593591openalex-snapshot
Pixel-Wise Deep Reinforcement Learning Approach for Ultrasound Image Denoising
inferred← citeshttps://doi.org/10.1109/ius54386.2022.9957624openalex-snapshot
Combined B-mode and Nakagami Images for Improved Discrimination of Breast Masses using Deep Learning
inferred← citeshttps://doi.org/10.1109/ius54386.2022.9957732openalex-snapshot
Machine Learning and Modeling of Ultrasonic Signals for High-Fidelity Data Compression
inferred← citeshttps://doi.org/10.1109/ius62464.2025.11201548openalex-snapshot
Deep Learning for Breast Mass Discrimination Using Multi-Modal Ultrasound Imaging with Automatic Lesion Segmentation
inferred← citeshttps://doi.org/10.1109/jbhi.2020.3008040openalex-snapshot
Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/jbhi.2025.3623032openalex-snapshot
Source-Resilient Joint Learning Framework for Preserving Stable Generalization on Diverse Ultrasonic Source Scenarios
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/southeastcon52093.2024.10500188openalex-snapshot
Vector-Quantization Variational Autoencoder Based Data Rate Reduction for Wireless Ultrasound Imaging Systems
inferred← citeshttps://doi.org/10.1109/tetci.2024.3377676openalex-snapshot
Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
inferred← citeshttps://doi.org/10.1109/tmi.2021.3116087openalex-snapshot
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image
inferred← citeshttps://doi.org/10.1109/tnnls.2021.3105367openalex-snapshot
Massive Ultrasonic Data Compression Using Wavelet Packet Transformation Optimized by Convolutional Autoencoders
inferred← citeshttps://doi.org/10.1109/tuson.2025.3638593openalex-snapshot
Parametric Quantitative Ultrasound-Based Deep Learning for Breast Cancer Diagnosis
inferred← citeshttps://doi.org/10.1109/uffc-js60046.2024.10793865openalex-snapshot
Guiding Ultrasound Breast Tumor Classification with Human-Specified Regions of Interest: A Differentiable Class Activation Map Approach
inferred← citeshttps://doi.org/10.1117/1.jmi.10.s2.s22410openalex-snapshot
Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization
inferred← citeshttps://doi.org/10.1117/1.jmi.12.s2.s22009openalex-snapshot
Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images
inferred← citeshttps://doi.org/10.1117/1.jmi.7.4.042805openalex-snapshot
Virtual clinical trials in medical imaging: a review
inferred← citeshttps://doi.org/10.1145/3611450.3611461openalex-snapshot
Adaptive weighted ensemble classifier for improving breast tumors classification based on ultrasound RF data
inferred← citeshttps://doi.org/10.1145/3777577.3777637openalex-snapshot
DCMNet: A Deep MLP-Based Ultrasound Breast Segmentation Network with Boundary Awareness
inferred← citeshttps://doi.org/10.1155/2022/1633858openalex-snapshot
Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors
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/10.1186/s12880-024-01307-3openalex-snapshot
Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach
inferred← citeshttps://doi.org/10.1186/s13634-020-00666-7openalex-snapshot
Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters
inferred← citeshttps://doi.org/10.1515/bmt-2025-0101openalex-snapshot
ICEEMDAN–power cepstrum framework for mean scatterer spacing estimation in breast and porcine liver tissues with microwave ablation validation
inferred← citeshttps://doi.org/10.15557/jou.2020.0005openalex-snapshot
Internet-based digital video atlas of sonographic findings for clinical and educational purposes
inferred← citeshttps://doi.org/10.15557/jou.2022.0013openalex-snapshot
Explaining a deep learning based breast ultrasound image classifier with saliency maps
inferred← citeshttps://doi.org/10.18502/aanbt.v6i1.18227openalex-snapshot
A Comprehensive Review on Breast Cancer Detection and Using Machine Learning Techniques: Methods, and Challenges Ahead
inferred← citeshttps://doi.org/10.2139/ssrn.6441745openalex-snapshot
Reinforcement Learning for Intelligent Image Restoration: A Survey on Algorithms, Applications, and Engineering Challenges
inferred← citeshttps://doi.org/10.3329/gubjse.v5i1.47903openalex-snapshot
Auto Segmentation of Breast Tumor in B-Mode Ultrasound Analysis
inferred← citeshttps://doi.org/10.3389/frai.2025.1572433openalex-snapshot
HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation
inferred← citeshttps://doi.org/10.3390/app10061900openalex-snapshot
Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare
inferred← citeshttps://doi.org/10.3390/app10228298openalex-snapshot
Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
inferred← citeshttps://doi.org/10.3390/app12104942openalex-snapshot
End-to-End Convolutional Neural Network Framework for Breast Ultrasound Analysis Using Multiple Parametric Images Generated from Radiofrequency Signals
inferred← citeshttps://doi.org/10.3390/app151910337openalex-snapshot
Curriculum Learning-Driven YOLO for Tumor Detection in Ultrasound Using Hierarchically Zoomed-In Images
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/bioengineering13070769openalex-snapshot
Coarse-to-Fine Curriculum Transfer Learning Using RF-Derived Ultrasound Representations for Small-Data Breast Tumor Detection
inferred← citeshttps://doi.org/10.3390/diagnostics9040182openalex-snapshot
Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging
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/jimaging12060258openalex-snapshot
Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions
inferred← citeshttps://doi.org/10.35234/fumbd.1142207openalex-snapshot
Ultrason RF Sinyallerinden Göğüs Kanserinin Derin Öğrenme Tabanlı Yaklaşımlarla Tespit Edilmesi
inferred← citeshttps://doi.org/10.4018/978-1-6684-7136-4.ch012openalex-snapshot
Digital Recognition of Breast Cancer Using TakhisisNet
inferred← citeshttps://doi.org/10.4018/978-1-6684-7544-7.ch066openalex-snapshot
Digital Recognition of Breast Cancer Using TakhisisNet
inferred← citeshttps://doi.org/10.4018/978-1-7998-3274-4.ch009openalex-snapshot
Digital Recognition of Breast Cancer Using TakhisisNet
inferred← citeshttps://doi.org/10.4018/979-8-3693-3218-4.ch013openalex-snapshot
Federated Learning for Breast Cancer Classification
inferred← citeshttps://doi.org/10.48550/arxiv.1804.02119openalex-snapshot
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
inferred← citeshttps://doi.org/10.48550/arxiv.2001.10061openalex-snapshot
Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net
inferred← citeshttps://doi.org/10.7507/1001-5515.202108015openalex-snapshot
[Design and implementation for portable ultrasound-aided breast cancer screening system].
inferred← citeshttps://doi.org/10.7717/peerj-cs.805openalex-snapshot
A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data
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