| 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 | 1261 | 2017 |
| 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 | 228 | 2016 |
| 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 | 140 | 2018 |
| 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 | 28 | 2016 |
| 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 | 23 | 2016 |
| 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 | 22 | 2022 |
| 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 | 18 | 2023 |
| 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 | 18 | 2015 |
| 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 | 11 | 2020 |
| Explainable image quality assessments in teledermatological photography R Jalaboi, O Winther, A Galimzianova Telemedicine and e-Health 29 (9), 1342-1348, 2023 | 10 | 2023 |
| 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 | 6 | 2018 |
| Robust mixture-parameter estimation for unsupervised segmentation of brain MR images A Galimzianova, Ž Špiclin, B Likar, F Pernuš International MICCAI Workshop on Medical Computer Vision, 84-94, 2013 | 6 | 2013 |
| 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 | 4 | 2019 |
| 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 | 4 | 2018 |
| 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 | 4 | 2015 |
| 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 | 3 | 2015 |
| 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 | 3 | 2013 |
| 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 | 3 | 2010 |
| 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, 2018 | 2 | 2018 |
| DermX: a Dermatological Diagnosis Explainability Dataset R Jalaboi, MO Arteaga, DR Jørgensen, I Manole, O Ionescu-Bozdog, ... | 1 | 2023 |
Žiga ŠpiclinUniversity of LjubljanaAdresă de e-mail confirmată pe fe.uni-lj.si