| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1779 | 2018 |
| A Question-Centric Model for Visual Question Answering in Medical Imaging MH Vu, T Löfstedt, T Nyholm, R Sznitman IEEE Transactions on Medical Imaging 39 (9), 2856 - 2868, 2020 | 51 | 2020 |
| Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation MH Vu, G Grimbergen, T Nyholm, T Löfstedt Medical Physics 47 (12), 6216-6231, 2020 | 41 | 2020 |
| TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks MH Vu, T Nyholm, T Löfstedt Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020 | 37 | 2020 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 34 | 2022 |
| Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain MH Vu, R Sznitman, T Nyholm, T Löfstedt CLEF 2019 Working Notes 2380, 2019 | 22 | 2019 |
| Design and simulation-based performance evaluation of a miniaturised implantable antenna for biomedical applications TA Aleef, YB Hagos, MH Vu, S Khawaldeh, U Pervaiz Micro & Nano Letters 12 (10), 821-826, 2017 | 19 | 2017 |
| Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation MH Vu, T Nyholm, T Löfstedt Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain …, 2021 | 17 | 2021 |
| Fast PET scan tumor segmentation using superpixels, principal component analysis and K-Means clustering Y Hagos, MH Vu, S Khawaldeh, U Pervaiz, T Aleef Methods and Protocols 1 (1), 7, 2018 | 14 | 2018 |
| A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation MH Vu, G Norman, T Nyholm, T Löfstedt IEEE Transactions on Medical Imaging 41 (6), 1320-1330, 2022 | 8 | 2022 |
| Complete End-To-End Low Cost Solution to a 3D Scanning System with Integrated Turntable S Khawaldeh, TA Aleef, U Pervaiz, MH Vu, YB Hagos arXiv preprint arXiv:1709.02247, 2017 | 7 | 2017 |
| End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation MH Vu, G Grimbergen, A Simkó, T Nyholm, T Löfstedt arXiv preprint arXiv:1910.07521, 2019 | 4 | 2019 |
| 3D Visualization System for the Anchor Rotation MH Vu, W He, SS Ge | 4* | 2012 |
| Activity monitoring and meal tracking for cardiac rehabilitation patients U Pervaiz, S Khawaldeh, TA Aleef, MH Vu, YB Hagos International Journal of Medical Engineering and Informatics 10 (3), 252-264, 2018 | 3 | 2018 |
| Localization Network and End-to-End Cascaded U-Nets for Kidney Tumor Segmentation MH Vu, G Grimbergen, A Simkó, T Nyholm, T Löfstedt Kidney Tumor Segmentation Challenge (KiTS19), 2019 | 2 | 2019 |
| Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation MH Vu, TA Aleef, U Pervaiz, YB Hagos, S Khawaldeh arXiv preprint arXiv:1710.01416, 2017 | 2 | 2017 |
| LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space L Tronchin, MH Vu, P Soda, T Löfstedt arXiv preprint arXiv:2307.11375, 2023 | 1 | 2023 |
| Compressing the Activation Maps in Deep Convolutional Neural Networks and the Regularization Effect of Compression MH Vu, A Garpebring, T Nyholm, T Löfstedt Transactions on Machine Learning Research 2835 (8856), 2024 | | 2024 |
| Resource efficient automatic segmentation of medical images MH Vu Umeå University, 2023 | | 2023 |
| Using synthetic images to augment small medical image datasets MH Vu, L Tronchin, T Nyholm, T Löfstedt | | 2023 |
