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Thomas J. Fuchs
Person information
- affiliation: Hasso Plattner Institute for Digital Health at Mount Sinai, USA
- affiliation (former): Memorial Sloan Kettering Cancer Center, New York, NY, USA
- affiliation (former): California Institute of Technology, Jet Propulsion Laboratory, Pasadena, CA, USA
- affiliation (former): ETH Zurich, Department of Computer Science, Switzerland
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2020 – today
- 2024
- [i25]Gabriele Campanella, Eugene Fluder, Jennifer Zeng, Chad M. Vanderbilt, Thomas J. Fuchs:
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling. CoRR abs/2403.04865 (2024) - [i24]George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann, Yi Kan Wang, Jeremy D. Kunz, Juan Retamero, Gerard Oakley, David S. Klimstra, Christopher Kanan, Matthew G. Hanna, Michal Zelechowski, Julian Viret, Neil A. Tenenholtz, James Brian Hall, Nicolò Fusi, Razik Yousfi, Peter Hamilton, William A. Moye, Eugene Vorontsov, Siqi Liu, Thomas J. Fuchs:
PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology. CoRR abs/2405.10254 (2024) - [i23]Shengjia Chen
, Gabriele Campanella, Abdulkadir Elmas, Aryeh Stock, Jennifer Zeng, Alexandros D. Polydorides, Adam Schoenfeld, Kuan-Lin Huang, Jane Houldsworth, Chad M. Vanderbilt, Thomas J. Fuchs:
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective. CoRR abs/2407.07841 (2024) - [i22]Eric Zimmermann, Eugene Vorontsov, Julian Viret, Adam Casson, Michal Zelechowski, George Shaikovski, Neil A. Tenenholtz, James Brian Hall, David S. Klimstra, Razik Yousfi, Thomas J. Fuchs, Nicoló Fusi, Siqi Liu, Kristen Severson:
Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology. CoRR abs/2408.00738 (2024) - 2023
- [c29]Adam Casson, Siqi Liu, Ran A. Godrich, Hamed Aghdam, Brandon Rothrock, Kasper Malfroid, Christopher Kanan, Thomas J. Fuchs:
Joint Breast Neoplasm Detection and Subtyping using Multi-Resolution Network Trained on Large-Scale H&E Whole Slide Images with Weak Labels. MIDL 2023: 18-38 - [i21]Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, Siqi Liu, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew G. Hanna, Juan Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David S. Klimstra, Brandon Rothrock, Thomas J. Fuchs:
Virchow: A Million-Slide Digital Pathology Foundation Model. CoRR abs/2309.07778 (2023) - [i20]Gabriele Campanella, Ricky Kwan, Eugene Fluder, Jennifer Zeng, Aryeh Stock
Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images. CoRR abs/2310.07033 (2023) - [i19]Eugenia Alleva, Isotta Landi, Leslee J. Shaw, Erwin P. Böttinger, Thomas J. Fuchs, Ipek Ensari:
Keyword-optimized Template Insertion for Clinical Information Extraction via Prompt-based Learning. CoRR abs/2310.20089 (2023) - 2022
- [i18]David Joon Ho, M. Herman Chui, Chad M. Vanderbilt, Ji Won Jung, Mark E. Robson, Chan-Sik Park, Jin Roh, Thomas J. Fuchs:
Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation. CoRR abs/2203.15015 (2022) - [i17]Gabriele Campanella, David Joon Ho, Ida Häggström, Anton S. Becker
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma. CoRR abs/2206.10573 (2022) - [i16]David Joon Ho, Narasimhan P. Agaram, Marc-Henri Jean, Stephanie D. Suser, Cynthia Chu, Chad M. Vanderbilt, Paul A. Meyers, Leonard H. Wexler, John H. Healey, Thomas J. Fuchs, Meera R. Hameed:
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction. CoRR abs/2208.04910 (2022) - [i15]Gabriele Campanella, Lucas Kook
Deep conditional transformation models for survival analysis. CoRR abs/2210.11366 (2022) - 2021
- [j10]David Joon Ho
Deep Multi-Magnification Networks for multi-class breast cancer image segmentation. Comput. Medical Imaging Graph. 88: 101866 (2021) - [j9]Peter J. Schüffler
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. J. Am. Medical Informatics Assoc. 28(9): 1874-1884 (2021) - [c28]Chao Feng, Chad M. Vanderbilt, Thomas J. Fuchs:
Nuc2Vec: Learning Representations of Nuclei in Histopathology Images with Contrastive Loss. MIDL 2021: 179-189 - [c27]Hassan Muhammad, Chensu Xie, Carlie S. Sigel, Michael Doukas, Lindsay Alpert, Amber Lea Simpson, Thomas J. Fuchs:
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, with Prognostic Stratification Boosting. MIDL 2021: 520-531 - [i14]Hassan Muhammad, Chensu Xie, Carlie S. Sigel, Michael Doukas, Lindsay Alpert, Thomas J. Fuchs:
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting. CoRR abs/2101.11085 (2021) - 2020
- [c26]David Joon Ho
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment. MICCAI (5) 2020: 540-549 - [c25]Chensu Xie, Hassan Muhammad, Chad M. Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, Thomas J. Fuchs:
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning. MIDL 2020: 843-856 - [i13]David Joon Ho, Narasimhan P. Agaram, Peter J. Schüffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed, Thomas J. Fuchs:
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment. CoRR abs/2007.01383 (2020)
2010 – 2019
- 2019
- [j8]Ida Häggström
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Medical Image Anal. 54: 253-262 (2019) - [c24]Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella
Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder. MICCAI (1) 2019: 604-612 - [c23]Chensu Xie, Chad M. Vanderbilt, Anne Grabenstetter, Thomas J. Fuchs:
VOCA: Cell Nuclei Detection In Histopathology Images By Vector Oriented Confidence Accumulation. MIDL 2019: 527-539 - [i12]Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Börner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber L. Simpson, Thomas J. Fuchs:
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary. CoRR abs/1903.05257 (2019) - [i11]David Joon Ho
Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation. CoRR abs/1910.13042 (2019) - 2018
- [j7]Gabriele Campanella
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology. Comput. Medical Imaging Graph. 65: 142-151 (2018) - [c22]Hassan Muhammad, Ida Häggström
Survival Modeling of Pancreatic Cancer with Radiology Using Convolutional Neural Networks. POCUS/BIVPCS/CuRIOUS/CPM@MICCAI 2018: 187-192 - [i10]Ida Haeggstroem
DeepRec: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem. CoRR abs/1804.07851 (2018) - [i9]Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs:
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology. CoRR abs/1805.06983 (2018) - 2016
- [j6]S. George Djorgovski, Matthew J. Graham, Ciro Donalek, Ashish Mahabal, Andrew J. Drake, Michael J. Turmon, Thomas J. Fuchs:
Real-time data mining of massive data streams from synoptic sky surveys. Future Gener. Comput. Syst. 59: 95-104 (2016) - [j5]Kyohei Otsu, Masahiro Ono, Thomas J. Fuchs, Ian Baldwin, Takashi Kubota:
Autonomous Terrain Classification With Co- and Self-Training Approach. IEEE Robotics Autom. Lett. 1(2): 814-819 (2016) - [c21]Andrew J. Schaumberg
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope. CIBB 2016: 42-58 - [c20]Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs:
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations. MLHC 2016: 191-208 - [i8]Thomas J. Fuchs, Joachim M. Buhmann:
Computational Pathology: Challenges and Promises for Tissue Analysis. CoRR abs/1601.00027 (2016) - [i7]S. George Djorgovski, Matthew J. Graham, Ciro Donalek, Ashish Mahabal, Andrew J. Drake, Michael J. Turmon, Thomas J. Fuchs:
Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys. CoRR abs/1601.04385 (2016) - [i6]Stefan Bauer, Nicolas Carion, Peter J. Schüffler, Thomas J. Fuchs, Peter J. Wild, Joachim M. Buhmann:
Multi-Organ Cancer Classification and Survival Analysis. CoRR abs/1606.00897 (2016) - [i5]Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs:
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations. CoRR abs/1608.00842 (2016) - 2015
- [j4]Arkadiusz Gertych
Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Medical Imaging Graph. 46: 197-208 (2015) - [c19]Michael S. Ryoo, Thomas J. Fuchs, Lu Xia, Jake K. Aggarwal, Larry H. Matthies:
Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me'. HRI 2015: 295-302 - [i4]Nikolaos Karianakis, Thomas J. Fuchs, Stefano Soatto:
Boosting Convolutional Features for Robust Object Proposals. CoRR abs/1503.06350 (2015) - [i3]Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas J. Fuchs, Hod Lipson:
Understanding Neural Networks Through Deep Visualization. CoRR abs/1506.06579 (2015) - 2014
- [c18]S. George Djorgovski, Ashish Mahabal, Ciro Donalek, Matthew J. Graham, Andrew J. Drake, Michael J. Turmon, Thomas J. Fuchs:
Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys. eScience 2014: 204-211 - [c17]Mélanie Rey, Volker Roth, Thomas J. Fuchs:
Sparse meta-Gaussian information bottleneck. ICML 2014: 910-918 - [i2]Michael S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal, Larry H. Matthies:
Early Recognition of Human Activities from First-Person Videos Using Onset Representations. CoRR abs/1406.5309 (2014) - 2013
- [c16]Ciro Donalek, S. George Djorgovski, Ashish Mahabal, Matthew J. Graham, Andrew J. Drake, Arun Kumar A., N. Sajeeth Philip, Thomas J. Fuchs, Michael J. Turmon, Michael Ting-Chang Yang, Giuseppe Longo:
Feature selection strategies for classifying high dimensional astronomical data sets. IEEE BigData 2013: 35-41 - [c15]Yumi Iwashita, Michael S. Ryoo, Thomas J. Fuchs, Curtis Padgett:
Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis. BMVC 2013 - [c14]Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona:
Quickly Boosting Decision Trees - Pruning Underachieving Features Early. ICML (3) 2013: 594-602 - [p2]Volker Roth
Structure Preserving Embedding of Dissimilarity Data. Similarity-Based Pattern Analysis and Recognition 2013: 157-177 - [p1]Peter J. Schüffler
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma. Similarity-Based Pattern Analysis and Recognition 2013: 219-245 - [i1]Ciro Donalek, Arun Kumar A., S. George Djorgovski, Ashish Mahabal, Matthew J. Graham, Thomas J. Fuchs, Michael J. Turmon, N. Sajeeth Philip, Michael Ting-Chang Yang, Giuseppe Longo:
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets. CoRR abs/1310.1976 (2013) - 2012
- [c13]Nicolas Hudson, Thomas Howard, Jeremy Ma, Abhinandan Jain, Max Bajracharya, Steven Myint, Calvin Kuo
End-to-end dexterous manipulation with deliberate interactive estimation. ICRA 2012: 2371-2378 - [c12]Paul Hebert, Nicolas Hudson, Jeremy Ma, Thomas Howard, Thomas J. Fuchs, Max Bajracharya, Joel W. Burdick:
Combined shape, appearance and silhouette for simultaneous manipulator and object tracking. ICRA 2012: 2405-2412 - 2011
- [j3]Thomas J. Fuchs, Joachim M. Buhmann:
Computational pathology: Challenges and promises for tissue analysis. Comput. Medical Imaging Graph. 35(7-8): 515-530 (2011) - 2010
- [j2]Sudhir Raman, Thomas J. Fuchs, Peter J. Wild
Infinite mixture-of-experts model for sparse survival regression with application to breast cancer. BMC Bioinform. 11(S-8): S8 (2010) - [c11]Verena Kaynig
Neuron geometry extraction by perceptual grouping in ssTEM images. CVPR 2010: 2902-2909 - [c10]Peter J. Schüffler
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma. DAGM-Symposium 2010: 202-211 - [c9]Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth:
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. ICML 2010: 1111-1118 - [c8]Verena Kaynig
Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data. MICCAI (2) 2010: 209-216
2000 – 2009
- 2009
- [j1]Stefan C. Saur, Hatem Alkadhi
Guided review by frequent itemset mining: additional evidence for plaque detection. Int. J. Comput. Assist. Radiol. Surg. 4(3): 263-271 (2009) - [c7]Thomas J. Fuchs, Joachim M. Buhmann:
Inter-active learning of randomized tree ensembles for object detection. ICCV Workshops 2009: 1370-1377 - [c6]Sudhir Raman, Thomas J. Fuchs, Peter J. Wild
The Bayesian group-Lasso for analyzing contingency tables. ICML 2009: 881-888 - [c5]Thomas J. Fuchs, Johannes Haybaeck, Peter J. Wild
Randomized Tree Ensembles for Object Detection in Computational Pathology. ISVC (1) 2009: 367-378 - [c4]Xenofon E. Floros, Thomas J. Fuchs, Markus P. Rechsteiner, Giatgen Spinas, Holger Moch, Joachim M. Buhmann:
Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue. MICCAI (1) 2009: 633-640 - 2008
- [c3]Thomas J. Fuchs, Tilman Lange, Peter J. Wild
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma. DAGM-Symposium 2008: 173-182 - [c2]Philipp Fürnstahl
Automatic and robust forearm segmentation using graph cuts. ISBI 2008: 77-80 - [c1]Thomas J. Fuchs, Peter J. Wild, Holger Moch, Joachim M. Buhmann:
Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients. MICCAI (2) 2008: 1-8
Coauthor Index
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