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Alfiia Galimzianova
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Deep learning for brain MRI segmentation: state of the art and future directions
Z Akkus, A Galimzianova, A Hoogi, DL Rubin, BJ Erickson
Journal of digital imaging 30, 449-459, 2017
11512017
A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound
K Lekadir, A Galimzianova, A Betriu, M del Mar Vila, L Igual, DL Rubin, ...
IEEE journal of biomedical and health informatics 21 (1), 48-55, 2016
2122016
A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus
Ž Lesjak, A Galimzianova, A Koren, M Lukin, F Pernuš, B Likar, Ž Špiclin
Neuroinformatics 16, 51-63, 2018
1172018
Stratified mixture modeling for segmentation of white-matter lesions in brain MR images
A Galimzianova, F Pernuš, B Likar, Ž Špiclin
NeuroImage 124, 1031-1043, 2016
272016
Combining unsupervised and supervised methods for lesion segmentation
T Jerman, A Galimzianova, F Pernuš, B Likar, Ž Špiclin
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016
222016
Robust estimation of unbalanced mixture models on samples with outliers
A Galimzianova, F Pernuš, B Likar, Ž Špiclin
IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (11), 2273 …, 2015
172015
Toward reduction in false-positive thyroid nodule biopsies with a deep learning–based risk stratification system using US cine-clip images
R Yamashita, T Kapoor, MN Alam, A Galimzianova, SA Syed, ...
Radiology: Artificial Intelligence 4 (3), e210174, 2022
112022
DermX: An end-to-end framework for explainable automated dermatological diagnosis
R Jalaboi, F Faye, M Orbes-Arteaga, D Jørgensen, O Winther, ...
Medical Image Analysis 83, 102647, 2023
92023
Quantitative framework for risk stratification of thyroid nodules with ultrasound: a step toward automated triage of thyroid cancer
A Galimzianova, SM Siebert, A Kamaya, DL Rubin, TS Desser
American Journal of Roentgenology 214 (4), 885-892, 2020
92020
Explainable image quality assessments in teledermatological photography
R Jalaboi, O Winther, A Galimzianova
Telemedicine and e-Health 29 (9), 1342-1348, 2023
82023
Robust mixture-parameter estimation for unsupervised segmentation of brain MR images
A Galimzianova, Ž Špiclin, B Likar, F Pernuš
Medical Computer Vision. Large Data in Medical Imaging: Third International …, 2014
62014
A multi-scale multiple sclerosis lesion change detection in a multi-sequence MRI
M Cheng, A Galimzianova, Ž Lesjak, Ž Špiclin, CB Lock, DL Rubin
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018
52018
Abstract P5-11-06: does hormone expression by IHC predict ER pathway activity? An analysis in a metastatic breast cancer patient cohort
SR Yang, A Van De Stolpe, A Van Brussel, H Van Ooijen, A Galimzianova, ...
Cancer Research 79 (4_Supplement), P5-11-06-P5-11-06, 2019
42019
Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions
A Galimzianova, Ž Lesjak, B Likar, F Pernuš, Ž Špiclin
Medical Imaging 2015: Image Processing 9413, 920-928, 2015
42015
Increased accuracy and reproducibility of MS lesion volume quantification by using publicly available BrainSeg3D image analysis software
Z Lesjak, A Galimzianova, B Likar, F Pernus, Z Spiclin
MULTIPLE SCLEROSIS JOURNAL 21, 500-501, 2015
32015
Automated segmentation of MS lesions in brain MR images using localized trimmed-likelihood estimation
A Galimzianova, Ž Špiclin, B Likar, F Pernuš
Medical Imaging 2013: Image Processing 8669, 937-943, 2013
32013
Does Hormone Expression by IHC predict ER Pathway Activity? An Analysis in A Metastatic Breast Cancer Patient Cohort.
SR Yang, A van Brussel, A van de Stolpe, H van Ooijen, A Galimzianova, ...
medicine 134 (7), e48-72, 2010
32010
Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions
A Galimzianova, Ž Lesjak, DL Rubin, B Likar, F Pernuš, Ž Špiclin
Journal of Medical Imaging 5 (1), 011007-011007, 2018
22018
Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound
A Galimzianova, SM Siebert, A Kamaya, TS Desser, DL Rubin
AMIA Annual Symposium Proceedings 2017, 734, 2017
22017
Special section on deep learning for biomedical and health informatics
D Ravı, C Wong, F Deligianni, M Berthelot, J Andreu-Perez, B Lo, G Yang, ...
Journal of Biomedical and Health Informatics, 0
1
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