ArticleScientific reports2025
A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
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- RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.Scientific reports · 2026Article
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- WTAM-YOLO: a YOLOv11-based method for pulmonary nodule detection.Scientific reports · 2026Article
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- Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.Scientific reports · 2026Article
- Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays.Scientific reports · 2026Article
- Enhanced YOLO-based framework and benchmarking for automated Plasmodium vivax detection.Parasitology research · 2026Article
- Enhancing WSI image classification with graph convolutional neural networks and model uncertainty modeling.BMC medical imaging · 2026Article
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- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An effective diagnosis system and suitable treatment planning require the precise segmentation of thyroid nodules in ultrasound imaging. The advancement of imaging technologies has not resolved traditional imaging challenges, which include noise issues, limited contrast, and dependency on operator choices, thus highlighting the need for automated, reliable solutions. The researchers developed TATHA, an innovative deep learning architecture dedicated to improving thyroid ultrasound image segmentation accuracy. The model is evaluated using the digital database of thyroid ultrasound images, which includes 99 cases across three subsets containing 134 labelled images for training, validation, and testing. It incorporates data pre-treatment procedures that reduce speckle noise and enhance contrast, while edge detection provides high-quality input for segmentation. TATHA outperforms U-Net, PSPNet, and Vision Transformers across various datasets and cross-validation folds, achieving superior Dice scores, accuracy, and AUC results. The distributed thyroid segmentation framework generates reliable predictions by combining results from multiple feature extraction units. The findings confirm that these advancements make TATHA an essential tool for clinicians and researchers in thyroid imaging and clinical applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.