197 related articles for article (PubMed ID: 32299197)
1. Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland.
Shin I; Kim YJ; Han K; Lee E; Kim HJ; Shin JH; Moon HJ; Youk JH; Kim KG; Kwak JY
Ultrasonography; 2020 Jul; 39(3):257-265. PubMed ID: 32299197
[TBL] [Abstract][Full Text] [Related]
2. Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography.
Wu H; Deng Z; Zhang B; Liu Q; Chen J
AJR Am J Roentgenol; 2016 Oct; 207(4):859-864. PubMed ID: 27340876
[TBL] [Abstract][Full Text] [Related]
3. Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists.
Park VY; Han K; Seong YK; Park MH; Kim EK; Moon HJ; Yoon JH; Kwak JY
Sci Rep; 2019 Nov; 9(1):17843. PubMed ID: 31780753
[TBL] [Abstract][Full Text] [Related]
4. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.
Wang L; Yang S; Yang S; Zhao C; Tian G; Gao Y; Chen Y; Lu Y
World J Surg Oncol; 2019 Jan; 17(1):12. PubMed ID: 30621704
[TBL] [Abstract][Full Text] [Related]
5. Sonographically suspicious thyroid nodules with initially benign cytologic results: the role of a core needle biopsy.
Ha EJ; Baek JH; Lee JH; Song DE; Kim JK; Shong YK; Hong SJ
Thyroid; 2013 Jun; 23(6):703-8. PubMed ID: 23544697
[TBL] [Abstract][Full Text] [Related]
6. Differentiate Thyroid Follicular Adenoma from Carcinoma with Combined Ultrasound Radiomics Features and Clinical Ultrasound Features.
Yu B; Li Y; Yu X; Ai Y; Jin J; Zhang J; Zhang Y; Zhu H; Xie C; Shen M; Yang Y; Jin X
J Digit Imaging; 2022 Oct; 35(5):1362-1372. PubMed ID: 35474555
[TBL] [Abstract][Full Text] [Related]
7. Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.
Chang Y; Paul AK; Kim N; Baek JH; Choi YJ; Ha EJ; Lee KD; Lee HS; Shin D; Kim N
Med Phys; 2016 Jan; 43(1):554. PubMed ID: 26745948
[TBL] [Abstract][Full Text] [Related]
8. Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study.
Lin AC; Liu Z; Lee J; Ranvier GF; Taye A; Owen R; Matteson DS; Lee D
Surgery; 2024 Jan; 175(1):121-127. PubMed ID: 37925261
[TBL] [Abstract][Full Text] [Related]
9. Differentiation of the Follicular Variant of Papillary Thyroid Carcinoma From Classic Papillary Thyroid Carcinoma: An Ultrasound Analysis and Complement to Fine-Needle Aspiration Cytology.
Ng SC; Kuo SF; Hua CC; Huang BY; Chiang KC; Chu YY; Hsueh C; Lin JD
J Ultrasound Med; 2018 Mar; 37(3):667-674. PubMed ID: 28880405
[TBL] [Abstract][Full Text] [Related]
10. Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.
Yu Q; Jiang T; Zhou A; Zhang L; Zhang C; Xu P
Eur Arch Otorhinolaryngol; 2017 Jul; 274(7):2891-2897. PubMed ID: 28389809
[TBL] [Abstract][Full Text] [Related]
11. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
[TBL] [Abstract][Full Text] [Related]
12. The follicular variant of papillary thyroid carcinoma: characteristics of preoperative ultrasonography and cytology.
Yoon JH; Kwon HJ; Kim EK; Moon HJ; Kwak JY
Ultrasonography; 2016 Jan; 35(1):47-54. PubMed ID: 26299354
[TBL] [Abstract][Full Text] [Related]
13. Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.
Prochazka A; Gulati S; Holinka S; Smutek D
Comput Med Imaging Graph; 2019 Jan; 71():9-18. PubMed ID: 30453231
[TBL] [Abstract][Full Text] [Related]
14. Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition.
Prochazka A; Gulati S; Holinka S; Smutek D
Technol Cancer Res Treat; 2019 Jan; 18():1533033819830748. PubMed ID: 30774015
[TBL] [Abstract][Full Text] [Related]
15. Classification of the thyroid nodules based on characteristic sonographic textural feature and correlated histopathology using hierarchical support vector machines.
Chen SJ; Chang CY; Chang KY; Tzeng JE; Chen YT; Lin CW; Hsu WC; Wei CK
Ultrasound Med Biol; 2010 Dec; 36(12):2018-26. PubMed ID: 21092831
[TBL] [Abstract][Full Text] [Related]
16. Pre-operative features of non-invasive follicular thyroid neoplasms with papillary-like nuclear features: An analysis of their cytological, Gene Expression Classifier and sonographic findings.
Song SJ; LiVolsi VA; Montone K; Baloch Z
Cytopathology; 2017 Dec; 28(6):488-494. PubMed ID: 29165886
[TBL] [Abstract][Full Text] [Related]
17. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images.
He X; Wei Y; Zhang H; Zhang T; Yuan F; Huang Z; Han F; Song B
Acad Radiol; 2020 Feb; 27(2):157-168. PubMed ID: 31147235
[TBL] [Abstract][Full Text] [Related]
18. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
Feng Z; Rong P; Cao P; Zhou Q; Zhu W; Yan Z; Liu Q; Wang W
Eur Radiol; 2018 Apr; 28(4):1625-1633. PubMed ID: 29134348
[TBL] [Abstract][Full Text] [Related]
19. Tumour growth rate of follicular thyroid carcinoma is not different from that of follicular adenoma.
Kim M; Han M; Lee JH; Song DE; Kim K; Baek JH; Shong YK; Kim WG
Clin Endocrinol (Oxf); 2018 Jun; 88(6):936-942. PubMed ID: 29509975
[TBL] [Abstract][Full Text] [Related]
20. An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland.
He Y; Zheng B; Peng W; Chen Y; Yu L; Huang W; Qin G
Eur Radiol; 2024 Apr; ():. PubMed ID: 38570381
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]