Research graph
References from Enhanced Swin Transformer Incorporating Multi-Head Self-Attention and Feature Refinement for Fetal Trimester Classification. Local targets link to admitted publications; unresolved targets remain external evidence.
Automated measurement of fetal head circumference using 2D ultrasound images.
10.1371/journal.pone.0200412 · External reference
Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters.
10.14738/tecs.123.16985 · External reference
The World Health Organization Fetal Growth Charts: A Multinational Longitudinal Study of Ultrasound Biometric Measurements and Estimated Fetal Weight.
10.1371/journal.pmed.1002220 · External reference
Automated Segmentation and Trimester-Based Classification of Fetal Head Circumference in Ultrasound Images Using Deep Learning Techniques.
10.1142/s021800142552038x · External reference
Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence.
10.1186/s12880-025-01709-x · External reference
A Hybrid Ensemble of Denoising Autoencoders and Deep Learning Models for Fetal Image Analysis.
10.7759/s44389-025-09506-x · External reference
From CNN to Transformer: A Review of Medical Image Segmentation Models.
10.1007/s10278-024-00981-7 · External reference
Ultrasound Image Analysis with Vision Transformers—Review.
10.3390/diagnostics14050542 · External reference
Is Attention all You Need in Medical Image Analysis? A Review.
10.1109/jbhi.2023.3348436 · External reference
10.1109/icrpset64863.2024.10955919
10.1109/icrpset64863.2024.10955919 · 2024 · External reference
Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning.
10.3390/diagnostics15172235 · External reference
10.1109/iccv48922.2021.00986
10.1109/iccv48922.2021.00986 · 2021 · External reference
Modified Multi-Head Attention Transformer (MMHAT) for Skin Image Classification.
10.20944/preprints202501.1723.v1 · External reference
Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.
10.1038/s41598-025-06526-4 · External reference
10.1002/mef2.38
10.1002/mef2.38 · External reference
Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.
10.3390/biomimetics8070519 · External reference
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.
10.1109/tmi.2017.2712367 · 2017 · External reference
Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models.
10.57197/jdr-2024-0005 · External reference
10.5220/0007385001810189
10.5220/0007385001810189 · 2019 · External reference
Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks.
10.3389/fmed.2024.1486995 · 2024 · External reference
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification.
External reference
Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers.
10.1038/s41598-023-44689-0 · External reference
Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.
10.1038/s41598-025-23100-0 · External reference
HAT: A Visual Transformer Model for Image Recognition Based on Hierarchical Attention Transformation.
10.1109/access.2023.3314573 · 2023 · External reference
Dynamic graph-based quantum feature selection for accurate fetal plane classification in ultrasound imaging.
10.1038/s41598-025-26835-y · External reference
Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.
10.1038/s41598-025-04631-y · External reference
Fetal Brain Planes Classification Using Deep Ensemble Transfer Learning from U-Net Segmented Fetal Neurosonography Images.
10.5815/ijigsp.2024.04.06 · External reference
Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes.
10.1038/s41598-020-67076-5 · External reference
An overview of mixing augmentation methods and augmentation strategies.
10.1007/s10462-022-10227-z · External reference
UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion.
10.1186/s42492-026-00215-3 · External reference
Swin Transformer–based intelligent fracture classification and radiographic assessment to support clinical decision-making.
10.1186/s12911-025-03327-7 · External reference
MSKd_Net: Multi-Head Attention-Based Swin Transformer for Kidney Diseases Classification.
10.1109/access.2024.3510634 · 2024 · External reference
Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.
10.1007/s10278-024-01140-8 · External reference
FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.
10.1371/journal.pone.0340286 · External reference
De-speckling of medical ultrasound image using metric-optimized knowledge distillation.
10.1038/s41598-025-07115-1 · External reference
Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images.
10.1186/s42492-024-00181-8 · External reference
K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach.
10.12688/f1000research.75469.2 · External reference
