237 related articles for article (PubMed ID: 35130517)
1. Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.
Jones MA; Faiz R; Qiu Y; Zheng B
Phys Med Biol; 2022 Feb; 67(5):. PubMed ID: 35130517
[No Abstract] [Full Text] [Related]
2. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.
Jones MA; Sadeghipour N; Chen X; Islam W; Zheng B
Med Phys; 2023 Dec; 50(12):7670-7683. PubMed ID: 37083190
[TBL] [Abstract][Full Text] [Related]
3. Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms.
Beuque MPL; Lobbes MBI; van Wijk Y; Widaatalla Y; Primakov S; Majer M; Balleyguier C; Woodruff HC; Lambin P
Radiology; 2023 Jun; 307(5):e221843. PubMed ID: 37338353
[TBL] [Abstract][Full Text] [Related]
4. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
[TBL] [Abstract][Full Text] [Related]
5. A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.
Danala G; Maryada SK; Islam W; Faiz R; Jones M; Qiu Y; Zheng B
Bioengineering (Basel); 2022 Jun; 9(6):. PubMed ID: 35735499
[TBL] [Abstract][Full Text] [Related]
6. Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.
Heidari M; Lakshmivarahan S; Mirniaharikandehei S; Danala G; Maryada SKR; Liu H; Zheng B
IEEE Trans Biomed Eng; 2021 Sep; 68(9):2764-2775. PubMed ID: 33493108
[TBL] [Abstract][Full Text] [Related]
7. Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.
Gai T; Thai T; Jones M; Jo J; Zheng B
J Xray Sci Technol; 2022; 30(2):377-388. PubMed ID: 35095015
[TBL] [Abstract][Full Text] [Related]
8. Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.
Gundreddy RR; Tan M; Qiu Y; Cheng S; Liu H; Zheng B
Med Phys; 2015 Jul; 42(7):4241-9. PubMed ID: 26133622
[TBL] [Abstract][Full Text] [Related]
9. Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features.
Cruz-Ramos C; García-Avila O; Almaraz-Damian JA; Ponomaryov V; Reyes-Reyes R; Sadovnychiy S
Entropy (Basel); 2023 Jun; 25(7):. PubMed ID: 37509938
[TBL] [Abstract][Full Text] [Related]
10. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
11. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.
Wang S; Sun Y; Li R; Mao N; Li Q; Jiang T; Chen Q; Duan S; Xie H; Gu Y
Eur Radiol; 2022 Jan; 32(1):639-649. PubMed ID: 34189600
[TBL] [Abstract][Full Text] [Related]
12. Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features.
Caballo M; Hernandez AM; Lyu SH; Teuwen J; Mann RM; van Ginneken B; Boone JM; Sechopoulos I
J Med Imaging (Bellingham); 2021 Mar; 8(2):024501. PubMed ID: 33796604
[No Abstract] [Full Text] [Related]
13. Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.
Islam W; Jones M; Faiz R; Sadeghipour N; Qiu Y; Zheng B
Tomography; 2022 Sep; 8(5):2411-2425. PubMed ID: 36287799
[TBL] [Abstract][Full Text] [Related]
14. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model.
Tan M; Pu J; Zheng B
Int J Comput Assist Radiol Surg; 2014 Nov; 9(6):1005-20. PubMed ID: 24664267
[TBL] [Abstract][Full Text] [Related]
15. [Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].
Liang C; Li M; Bian Z; Lv W; Zeng D; Ma J
Nan Fang Yi Ke Da Xue Xue Bao; 2019 Jan; 39(1):88-92. PubMed ID: 30692072
[TBL] [Abstract][Full Text] [Related]
16. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
Qiu Y; Yan S; Gundreddy RR; Wang Y; Cheng S; Liu H; Zheng B
J Xray Sci Technol; 2017; 25(5):751-763. PubMed ID: 28436410
[TBL] [Abstract][Full Text] [Related]
17. Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.
Hamdi M; Senan EM; Jadhav ME; Olayah F; Awaji B; Alalayah KM
Diagnostics (Basel); 2023 Jul; 13(13):. PubMed ID: 37443652
[TBL] [Abstract][Full Text] [Related]
18. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study.
Gong J; Liu JY; Jiang YJ; Sun XW; Zheng B; Nie SD
Med Phys; 2018 Dec; 45(12):5472-5481. PubMed ID: 30317652
[TBL] [Abstract][Full Text] [Related]
19. Pattern classification for breast lesion on FFDM by integration of radiomics and deep features.
Zhang X; Liang C; Zeng D; Jiang X; Zhong R; Lan Y; Ma J; Bai L
Comput Med Imaging Graph; 2021 Jun; 90():101922. PubMed ID: 34049119
[TBL] [Abstract][Full Text] [Related]
20. Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.
Heidari M; Mirniaharikandehei S; Liu W; Hollingsworth AB; Liu H; Zheng B
IEEE Trans Med Imaging; 2020 Apr; 39(4):1235-1244. PubMed ID: 31603818
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
