Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention

[1] Laetitia G(2020)Combinatorial therapies in thyroid cancer: an overview of preclinical and clinical progresses Cells 9 830-579

[2] Saussez S(2020)Efficacy and safety of long-term universal salt iodization on thyroid disorders: epidemiological evidence from 31 provinces of Mainland China Thyroid 30 568-40

[3] Journe F(2018)Combination of ultrasound elastography with TI-RADS in the diagnosis of small thyroid nodules (≤ 10 mm): a new method to increase the diagnostic performance Eur J Radiol 109 33-3090

[4] Li Y(2022)Tumor microenvironment, radiology, and artificial intelligence: should we consider tumor periphery? J Ultrasound Med 41 3079-63496

[5] Teng D(2023)Assessment of encoder–decoder-based segmentation models for thyroid ultrasound images Med Biol Eng Comput 61 2159-5127

[6] Ba J(2023)Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset Appl Intell 53 19708-392

[7] Du Y-R(2023)BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation Comput Methods Progr Biomed 238 63482-6124

[8] Chen-Li J(2023)FCG-Net: an innovative full-scale connected network for thyroid nodule segmentation in ultrasound images Biomed Signal Process Control 86 5119-470

[9] Yang W(2023)ACU2E-Net: a novel predict–refine attention network for segmentation of soft-tissue structures in ultrasound images Comput Biol Med 157 382-8937

[10] Mohammadi A(2020)Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning Ieee Access 8 6113-45

[1] Laetitia G(2020)Combinatorial therapies in thyroid cancer: an overview of preclinical and clinical progresses Cells 9 830-579

[2] Saussez S(2020)Efficacy and safety of long-term universal salt iodization on thyroid disorders: epidemiological evidence from 31 provinces of Mainland China Thyroid 30 568-40

[3] Journe F(2018)Combination of ultrasound elastography with TI-RADS in the diagnosis of small thyroid nodules (≤ 10 mm): a new method to increase the diagnostic performance Eur J Radiol 109 33-3090

[4] Li Y(2022)Tumor microenvironment, radiology, and artificial intelligence: should we consider tumor periphery? J Ultrasound Med 41 3079-63496

[5] Teng D(2023)Assessment of encoder–decoder-based segmentation models for thyroid ultrasound images Med Biol Eng Comput 61 2159-5127

[6] Ba J(2023)Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset Appl Intell 53 19708-392

[7] Du Y-R(2023)BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation Comput Methods Progr Biomed 238 63482-6124

[8] Chen-Li J(2023)FCG-Net: an innovative full-scale connected network for thyroid nodule segmentation in ultrasound images Biomed Signal Process Control 86 5119-470

[9] Yang W(2023)ACU2E-Net: a novel predict–refine attention network for segmentation of soft-tissue structures in ultrasound images Comput Biol Med 157 382-8937

[10] Mohammadi A(2020)Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning Ieee Access 8 6113-45