Machine learning and vision transformers for thyroid carcinoma diagnosis: A review

Y Habchi, H Kheddar, Y Himeur, A Boukabou… - arXiv preprint arXiv …, 2024 - arxiv.org
The growing interest in developing smart diagnostic systems to help medical experts
process extensive data for treating incurable diseases has been notable. In particular, the …

MLMSeg: a multi-view learning model for ultrasound thyroid nodule segmentation

G Chen, G Tan, M Duan, B Pu, H Luo, S Li… - Computers in Biology and …, 2024 - Elsevier
Accurate segmentation of the thyroid gland in ultrasound images is an essential initial step
in distinguishing between benign and malignant nodules, thus facilitating early diagnosis …

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach

NG Inan, O Kocadağlı, D Yıldırım, İ Meşe… - Computer Methods and …, 2024 - Elsevier
Background and objectives Early detection and diagnosis of thyroid nodule types are
important because they can be treated more effectively in their early stages. The types of …

Ultrasound Image Analysis with Vision Transformers

M Vafaeezadeh, H Behnam, P Gifani - Diagnostics, 2024 - mdpi.com
Ultrasound (US) has become a widely used imaging modality in clinical practice,
characterized by its rapidly evolving technology, advantages, and unique challenges, such …

Explainable classification of benign-malignant pulmonary nodules with neural networks and information bottleneck

H Zhu, W Liu, Z Gao, H Zhang - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Computerized tomography (CT) is a clinically primary technique to differentiate benign-
malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary …

Do as Sonographers Think: Contrast-enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness

F Chen, H Han, P Wan, L Chen, W Kong… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular
distribution and blood flow perfusion, thereby holding clinical significance in distinguishing …

Label-Decoupled Medical Image Segmentation with Spatial-Channel Graph Convolution and Dual Attention Enhancement

Q Jiang, H Ye, B Yang, F Cao - IEEE Journal of Biomedical and …, 2024 - ieeexplore.ieee.org
Deep learning-based methods have been widely used in medical image segmentation
recently. However, existing works are usually difficult to simultaneously capture global long …

[HTML][HTML] A narrative review of deep learning in thyroid imaging: current progress and future prospects

WT Yang, BY Ma, Y Chen - Quantitative Imaging in Medicine and …, 2024 - ncbi.nlm.nih.gov
Methods We searched for English-language articles published between April 2018 and
September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The …

Pathologist-Like Explanations Unveiled: An Explainable Deep Learning System for White Blood Cell Classification

AS Pal, D Biswas, J Mahapatra… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
Despite of achieving remarkable accuracy, the capability of deep learning models for robust
prediction of explanations remains largely unexplored in white blood cells (WBCs) …

EBTNet: Efficient Bilateral Token Mixer Network for Fetal Cardiac Ultrasound Image Segmentation

Y Pan, L Niu, X Yang, Q Niu, B Chen - IEEE Access, 2024 - ieeexplore.ieee.org
Fetal cardiac ultrasound apical 4-chamber (A4C) view segmentation with deep learning
technique is a crucial auxiliary to diagnosing congenital heart disease. Due to the …