Thomas J. Fuchs
Title
Cited by
Cited by
Year
Understanding neural networks through deep visualization
J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson
arXiv preprint arXiv:1506.06579, 2015
13042015
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
G Campanella, MG Hanna, L Geneslaw, A Miraflor, VWK Silva, KJ Busam, ...
Nature medicine 25 (8), 1301-1309, 2019
2142019
TAK1 suppresses a NEMO-dependent but NF-κB-independent pathway to liver cancer
K Bettermann, M Vucur, J Haybaeck, C Koppe, J Janssen, F Heymann, ...
Cancer cell 17 (5), 481-496, 2010
2132010
Computational pathology: Challenges and promises for tissue analysis
TJ Fuchs, JM Buhmann
Computerized Medical Imaging and Graphics 35 (7-8), 515-530, 2011
1792011
Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer
I Cima, R Schiess, P Wild, M Kaelin, P Schüffler, V Lange, P Picotti, ...
Proceedings of the National Academy of Sciences 108 (8), 3342-3347, 2011
1752011
Quickly boosting decision trees–pruning underachieving features early
R Appel, T Fuchs, P Dollár, P Perona
International conference on machine learning, 594-602, 2013
1372013
Neuron geometry extraction by perceptual grouping in sstem images
V Kaynig, T Fuchs, JM Buhmann
2010 IEEE computer society conference on computer vision and pattern …, 2010
1202010
Prognostic relevance of Wnt-inhibitory factor-1 (WIF1) and Dickkopf-3 (DKK3) promoter methylation in human breast cancer
J Veeck, PJ Wild, T Fuchs, PJ Schüffler, A Hartmann, R Knüchel, E Dahl
BMC cancer 9 (1), 217, 2009
1062009
Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects
H Muhammad, TJ Fuchs, N De Cuir, CG De Moraes, DM Blumberg, ...
Journal of glaucoma 26 (12), 1086, 2017
1052017
Robot-centric activity prediction from first-person videos: What will they do to me?
MS Ryoo, TJ Fuchs, L Xia, JK Aggarwal, L Matthies
2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2015
1022015
Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients
TJ Fuchs, PJ Wild, H Moch, JM Buhmann
International Conference on Medical Image Computing and Computer-Assisted …, 2008
932008
The Bayesian group-lasso for analyzing contingency tables
S Raman, TJ Fuchs, PJ Wild, E Dahl, V Roth
Proceedings of the 26th annual international conference on machine learning …, 2009
902009
Nuclear karyopherin α2 expression predicts poor survival in patients with advanced breast cancer irrespective of treatment intensity
O Gluz, P Wild, R Meiler, R Diallo‐Danebrock, E Ting, S Mohrmann, ...
International journal of cancer 123 (6), 1433-1438, 2008
732008
Machine learning approaches to analyze histological images of tissues from radical prostatectomies
A Gertych, N Ing, Z Ma, TJ Fuchs, S Salman, S Mohanty, S Bhele, ...
Computerized Medical Imaging and Graphics 46, 197-208, 2015
672015
Aerosols transmit prions to immunocompetent and immunodeficient mice
J Haybaeck, M Heikenwalder, B Klevenz, P Schwarz, I Margalith, C Bridel, ...
PLoS Pathog 7 (1), e1001257, 2011
672011
A seven-marker signature and clinical outcome in malignant melanoma: a large-scale tissue-microarray study with two independent patient cohorts
S Meyer, TJ Fuchs, AK Bosserhoff, F Hofstädter, A Pauer, V Roth, ...
PloS one 7 (6), e38222, 2012
602012
Nuclear detection of Y-boxprotein-1 (YB-1) closely associates with progesterone receptor negativity and is a strong adverse survival factor in human breast cancer
E Dahl, A En-Nia, F Wiesmann, R Krings, S Djudjaj, E Breuer, T Fuchs, ...
BMC cancer 9 (1), 410, 2009
592009
TMARKER: A free software toolkit for histopathological cell counting and staining estimation
PJ Schüffler, TJ Fuchs, CS Ong, PJ Wild, NJ Rupp, JM Buhmann
Journal of pathology informatics 4 (Suppl), 2013
582013
A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
SR Debats, D Luo, LD Estes, TJ Fuchs, KK Caylor
Remote Sensing of Environment 179, 210-221, 2016
562016
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem
I Häggström, CR Schmidtlein, G Campanella, TJ Fuchs
Medical image analysis 54, 253-262, 2019
512019
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Articles 1–20