| Evidential Deep Learning to Quantify Classification Uncertainty M Sensoy, L Kaplan, M Kandemir Advances in Neural Information Processing Systems (NeurIPS), 3179-3189, 2018 | 1003 | 2018 |
| Automatic segmentation of colon glands using object-graphs C Gunduz-Demir, M Kandemir, AB Tosun, C Sokmensuer Medical image analysis 14 (1), 1-12, 2010 | 192 | 2010 |
| Computer-aided diagnosis from weak supervision: A benchmarking study M Kandemir, FA Hamprecht Computerized medical imaging and graphics 42, 44-50, 2015 | 146 | 2015 |
| An augmented reality interface to contextual information A Ajanki, M Billinghurst, H Gamper, T Järvenpää, M Kandemir, S Kaski, ... Virtual reality 15, 161-173, 2011 | 131 | 2011 |
| Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection AB Tosun, M Kandemir, C Sokmensuer, C Gunduz-Demir Pattern Recognition 42 (6), 1104-1112, 2009 | 117 | 2009 |
| Empowering multiple instance histopathology cancer diagnosis by cell graphs M Kandemir, C Zhang, FA Hamprecht Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 82 | 2014 |
| Asymmetric Transfer Learning with Deep Gaussian Processes M Kandemir International Conference on Machine Learning, 730-738, 2015 | 60 | 2015 |
| Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals JP Kauppi, M Kandemir, VM Saarinen, L Hirvenkari, L Parkkonen, A Klami, ... NeuroImage 112, 288-298, 2015 | 59 | 2015 |
| Deep Active Learning with Adaptive Acquisition M Haußmann, FA Hamprecht, M Kandemir International Joint Conference on Artificial Intelligence (IJCAI), arXiv …, 2019 | 49 | 2019 |
| Multi-task and multi-view learning of user state M Kandemir, A Vetek, M Gönen, A Klami, S Kaski Neurocomputing 139, 97-106, 2014 | 48 | 2014 |
| Gaussian process density counting from weak supervision M von Borstel, M Kandemir, P Schmidt, MK Rao, K Rajamani, ... Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 44 | 2016 |
| Variational Bayesian Multiple Instance Learning with Gaussian Processes M Haußmann, FA Hamprecht, M Kandemir Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 40 | 2017 |
| Digital pathology: Multiple instance learning can detect Barrett's cancer M Kandemir, A Feuchtinger, A Walch, FA Hamprecht 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 1348-1351, 2014 | 40 | 2014 |
| Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation M Haussmann, FA Hamprecht, M Kandemir Uncertainty in Artificial Intelligence (UAI), arXiv preprint arXiv:1805.07654, 2019 | 36* | 2019 |
| Inferring object relevance from gaze in dynamic scenes M Kandemir, VM Saarinen, S Kaski Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications …, 2010 | 25 | 2010 |
| Instance Label Prediction by Dirichlet Process Multiple Instance Learning. M Kandemir, FA Hamprecht UAI, 380-389, 2014 | 23 | 2014 |
| Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes M Haussmann, S Gerwinn, A Look, B Rakitsch, M Kandemir International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 | 22 | 2021 |
| Contextual information access with augmented reality A Ajanki, M Billinghurst, T Järvenpää, M Kandemir, S Kaski, M Koskela, ... 2010 IEEE International Workshop on Machine Learning for Signal Processing …, 2010 | 22 | 2010 |
| Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks O Narmanlioglu, E Zeydan, M Kandemir, T Kranda 2017 8th International Conference on the Network of the Future (NOF), 73-78, 2017 | 20 | 2017 |
| Multiple instance learning: Robust validation on retinopathy of prematurity P Rani, R Elagiri Ramalingam, KT Rajamani, M Kandemir, D Singh Int J Ctrl Theory Appl 9, 451-459, 2016 | 20 | 2016 |
Fred A. HamprechtProfessor, Heidelberg UniversityVerified email at iwr.uni-heidelberg.de
