| Nipype KJ Gorgolewski, O Esteban, CJ Markiewicz, E Ziegler, DG Ellis, MP Notter, ... Software, 2018 | 137 | 2018 |
| Regional infant brain development: an MRI-based morphometric analysis in 3 to 13 month olds M Choe, S Ortiz-Mantilla, N Makris, M Gregas, J Bacic, D Haehn, ... Cerebral Cortex 23 (9), 2100-2117, 2013 | 114 | 2013 |
| Design and evaluation of interactive proofreading tools for connectomics D Haehn, S Knowles-Barley, M Roberts, J Beyer, N Kasthuri, JW Lichtman, ... IEEE transactions on visualization and computer graphics 20 (12), 2466-2475, 2014 | 66 | 2014 |
| Neuroimaging in the Browser using the X Toolkit D Haehn, N Rannou, B Ahtam, E Grant, R Pienaar Frontiers in Neuroinformatics, 2012 | 61 | 2012 |
| Evaluating ‘graphical perception’with CNNs D Haehn, J Tompkin, H Pfister IEEE transactions on visualization and computer graphics 25 (1), 641-650, 2018 | 60 | 2018 |
| Fast mitochondria detection for connectomics V Casser, K Kang, H Pfister, D Haehn Medical Imaging with Deep Learning, 111-120, 2020 | 54* | 2020 |
| Neuroblocks–visual tracking of segmentation and proofreading for large connectomics projects AK Ai-Awami, J Beyer, D Haehn, N Kasthuri, JW Lichtman, H Pfister, ... IEEE transactions on visualization and computer graphics 22 (1), 738-746, 2015 | 44 | 2015 |
| Altered structural brain networks in tuberous sclerosis complex K Im, B Ahtam, D Haehn, JM Peters, SK Warfield, M Sahin, P Ellen Grant Cerebral cortex 26 (5), 2046-2058, 2016 | 43 | 2016 |
| Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning F Lekschas, B Peterson, D Haehn, E Ma, N Gehlenborg, H Pfister Computer Graphics Forum 39 (3), 167-179, 2020 | 38 | 2020 |
| Scalable interactive visualization for connectomics D Haehn, J Hoffer, B Matejek, A Suissa-Peleg, AK Al-Awami, L Kamentsky, ... Informatics 4 (3), 29, 2017 | 29 | 2017 |
| Guided Proofreading of Automatic Segmentations for Connectomics D Haehn, V Kaynig, J Tompkin, JW Lichtman, H Pfister IEEE Computer Vision and Pattern Recognition (CVPR), 2017 | 29 | 2017 |
| How machine learning is powering neuroimaging to improve brain health NM Singh, JB Harrod, S Subramanian, M Robinson, K Chang, ... Neuroinformatics 20 (4), 943-964, 2022 | 27 | 2022 |
| Biologically-constrained graphs for global connectomics reconstruction B Matejek, D Haehn, H Zhu, D Wei, T Parag, H Pfister Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 24 | 2019 |
| Automatic neural reconstruction from petavoxel of electron microscopy data A Suissa-Peleg, D Haehn, S Knowles-Barley, V Kaynig, TR Jones, ... Microscopy and Microanalysis 22 (S3), 536-537, 2016 | 20 | 2016 |
| Two stream active query suggestion for active learning in connectomics Z Lin, D Wei, WD Jang, S Zhou, X Chen, X Wang, R Schalek, D Berger, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 19 | 2020 |
| Slice: drop: collaborative medical imaging in the browser D Haehn ACM SIGGRAPH 2013 computer animation festival, 1-1, 2013 | 19 | 2013 |
| Imaging a 1 mm3Volume of Rat Cortex Using a MultiBeam SEM R Schalek, D Lee, N Kasthuri, A Peleg, T Jones, V Kaynig, D Haehn, ... Microscopy and Microanalysis 22 (S3), 582-583, 2016 | 18 | 2016 |
| ChRIS-A web-based neuroimaging and informatics system for collecting, organizing, processing, visualizing and sharing of medical data R Pienaar, N Rannou, J Bernal, D Hähn, PE Grant 2015 37th Annual International Conference of the IEEE Engineering in …, 2015 | 18 | 2015 |
| MedShapeNet--A large-scale dataset of 3D medical shapes for computer vision J Li, Z Zhou, J Yang, A Pepe, C Gsaxner, G Luijten, C Qu, T Zhang, ... arXiv preprint arXiv:2308.16139, 2023 | 17 | 2023 |
| Modern scientific visualizations on the web L Franke, D Haehn Informatics 7 (4), 37, 2020 | 14 | 2020 |
Hanspeter PfisterAn Wang Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
