836 related articles for article (PubMed ID: 30361936)
1. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.
Ha R; Chin C; Karcich J; Liu MZ; Chang P; Mutasa S; Pascual Van Sant E; Wynn RT; Connolly E; Jambawalikar S
J Digit Imaging; 2019 Oct; 32(5):693-701. PubMed ID: 30361936
[TBL] [Abstract][Full Text] [Related]
2. Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.
Ha R; Chang P; Karcich J; Mutasa S; Van Sant EP; Connolly E; Chin C; Taback B; Liu MZ; Jambawalikar S
Ann Surg Oncol; 2018 Oct; 25(10):3037-3043. PubMed ID: 29978368
[TBL] [Abstract][Full Text] [Related]
3. Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.
Ha R; Chang P; Karcich J; Mutasa S; Fardanesh R; Wynn RT; Liu MZ; Jambawalikar S
J Digit Imaging; 2018 Dec; 31(6):851-856. PubMed ID: 29696472
[TBL] [Abstract][Full Text] [Related]
4. Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.
Ha R; Chang P; Mutasa S; Karcich J; Goodman S; Blum E; Kalinsky K; Liu MZ; Jambawalikar S
J Magn Reson Imaging; 2019 Feb; 49(2):518-524. PubMed ID: 30129697
[TBL] [Abstract][Full Text] [Related]
5. Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.
Ha R; Chang P; Mema E; Mutasa S; Karcich J; Wynn RT; Liu MZ; Jambawalikar S
J Digit Imaging; 2019 Feb; 32(1):141-147. PubMed ID: 30076489
[TBL] [Abstract][Full Text] [Related]
6. A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.
Liu MZ; Mutasa S; Chang P; Siddique M; Jambawalikar S; Ha R
Magn Reson Imaging; 2020 Nov; 73():148-151. PubMed ID: 32889091
[TBL] [Abstract][Full Text] [Related]
7. Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.
Ha R; Mutasa S; Karcich J; Gupta N; Pascual Van Sant E; Nemer J; Sun M; Chang P; Liu MZ; Jambawalikar S
J Digit Imaging; 2019 Apr; 32(2):276-282. PubMed ID: 30706213
[TBL] [Abstract][Full Text] [Related]
8. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images.
Zhou J; Luo LY; Dou Q; Chen H; Chen C; Li GJ; Jiang ZF; Heng PA
J Magn Reson Imaging; 2019 Oct; 50(4):1144-1151. PubMed ID: 30924997
[TBL] [Abstract][Full Text] [Related]
9. Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation.
Trivizakis E; Manikis GC; Nikiforaki K; Drevelegas K; Constantinides M; Drevelegas A; Marias K
IEEE J Biomed Health Inform; 2019 May; 23(3):923-930. PubMed ID: 30561355
[TBL] [Abstract][Full Text] [Related]
10. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
El Adoui M; Drisis S; Benjelloun M
Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1491-1500. PubMed ID: 32556920
[TBL] [Abstract][Full Text] [Related]
11. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.
Hamm CA; Wang CJ; Savic LJ; Ferrante M; Schobert I; Schlachter T; Lin M; Duncan JS; Weinreb JC; Chapiro J; Letzen B
Eur Radiol; 2019 Jul; 29(7):3338-3347. PubMed ID: 31016442
[TBL] [Abstract][Full Text] [Related]
12. Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.
Ha R; Chang P; Karcich J; Mutasa S; Pascual Van Sant E; Liu MZ; Jambawalikar S
Acad Radiol; 2019 Apr; 26(4):544-549. PubMed ID: 30072292
[TBL] [Abstract][Full Text] [Related]
13. Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.
Spuhler KD; Ding J; Liu C; Sun J; Serrano-Sosa M; Moriarty M; Huang C
Magn Reson Med; 2019 Aug; 82(2):786-795. PubMed ID: 30957936
[TBL] [Abstract][Full Text] [Related]
14. Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images.
He Y; Guo J; Ding X; van Ooijen PMA; Zhang Y; Chen A; Oudkerk M; Xie X
Eur Radiol; 2019 Oct; 29(10):5441-5451. PubMed ID: 30859281
[TBL] [Abstract][Full Text] [Related]
15. New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy.
Adoui ME; Drisis S; Benjelloun M
Curr Med Imaging; 2022; 19(5):526-533. PubMed ID: 36529908
[TBL] [Abstract][Full Text] [Related]
16. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.
Truhn D; Schrading S; Haarburger C; Schneider H; Merhof D; Kuhl C
Radiology; 2019 Feb; 290(2):290-297. PubMed ID: 30422086
[TBL] [Abstract][Full Text] [Related]
17. MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.
Mutasa S; Chang PD; Ruzal-Shapiro C; Ayyala R
J Digit Imaging; 2018 Aug; 31(4):513-519. PubMed ID: 29404850
[TBL] [Abstract][Full Text] [Related]
18. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.
Moon WK; Lee YW; Ke HH; Lee SH; Huang CS; Chang RF
Comput Methods Programs Biomed; 2020 Jul; 190():105361. PubMed ID: 32007839
[TBL] [Abstract][Full Text] [Related]
19. An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.
Hoseini F; Shahbahrami A; Bayat P
J Digit Imaging; 2018 Oct; 31(5):738-747. PubMed ID: 29488179
[TBL] [Abstract][Full Text] [Related]
20. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.
Zhang Y; Chen JH; Lin Y; Chan S; Zhou J; Chow D; Chang P; Kwong T; Yeh DC; Wang X; Parajuli R; Mehta RS; Wang M; Su MY
Eur Radiol; 2021 Apr; 31(4):2559-2567. PubMed ID: 33001309
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
