| Fine-grained visual classification of aircraft S Maji, E Rahtu, J Kannala, M Blaschko, A Vedaldi arXiv preprint arXiv:1306.5151, 2013 | 2517 | 2013 |
| The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks M Berman, A Rannen Triki, MB Blaschko Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 1119 | 2018 |
| Beyond sliding windows: Object localization by efficient subwindow search CH Lampert, MB Blaschko, T Hofmann 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008 | 1050 | 2008 |
| A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images JI Orlando, E Prokofyeva, MB Blaschko IEEE transactions on Biomedical Engineering 64 (1), 16-27, 2016 | 555 | 2016 |
| Efficient subwindow search: A branch and bound framework for object localization CH Lampert, MB Blaschko, T Hofmann IEEE transactions on pattern analysis and machine intelligence 31 (12), 2129 …, 2009 | 511 | 2009 |
| Learning to localize objects with structured output regression MB Blaschko, CH Lampert Computer Vision–ECCV 2008: 10th European Conference on Computer Vision …, 2008 | 477 | 2008 |
| Encoder based lifelong learning A Rannen, R Aljundi, MB Blaschko, T Tuytelaars Proceedings of the IEEE international conference on computer vision, 1320-1328, 2017 | 428 | 2017 |
| Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 417 | 2019 |
| Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index T Eelbode, J Bertels, M Berman, D Vandermeulen, F Maes, R Bisschops, ... IEEE transactions on medical imaging 39 (11), 3679-3690, 2020 | 361 | 2020 |
| An ensemble deep learning based approach for red lesion detection in fundus images JI Orlando, E Prokofyeva, M Del Fresno, MB Blaschko Computer methods and programs in biomedicine 153, 115-127, 2018 | 340 | 2018 |
| Metrics reloaded: recommendations for image analysis validation L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ... Nature methods, 1-18, 2024 | 317* | 2024 |
| Combining local and global image features for object class recognition DA Lisin, MA Mattar, MB Blaschko, EG Learned-Miller, MC Benfield 2005 IEEE computer society conference on computer vision and pattern …, 2005 | 317 | 2005 |
| Correlational spectral clustering MB Blaschko, CH Lampert 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008 | 276 | 2008 |
| Unsupervised object discovery: A comparison T Tuytelaars, CH Lampert, MB Blaschko, W Buntine International journal of computer vision 88, 284-302, 2010 | 258 | 2010 |
| Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021 | 236* | 2021 |
| Learning a category independent object detection cascade E Rahtu, J Kannala, M Blaschko 2011 international conference on Computer Vision, 1052-1059, 2011 | 220 | 2011 |
| Convolutional neural network transfer for automated glaucoma identification JI Orlando, E Prokofyeva, M del Fresno, MB Blaschko 12th international symposium on medical information processing and analysis …, 2017 | 167 | 2017 |
| Learning fully-connected CRFs for blood vessel segmentation in retinal images JI Orlando, M Blaschko Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 166 | 2014 |
| R-gap: Recursive gradient attack on privacy J Zhu, M Blaschko International Conference on Learning Representations (ICLR), 2021 | 155 | 2021 |
| B-test: A non-parametric, low variance kernel two-sample test W Zaremba, A Gretton, M Blaschko Advances in neural information processing systems 26, 2013 | 147 | 2013 |
Maxim BermanGoogle確認したメール アドレス: google.com