Thomas J. Fuchs

发表

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Andrew J. Schaumberg, Wendy C. Juarez-Nicanor, Sarah J. Choudhury, 2020, Modern Pathology.

The Bayesian group-Lasso for analyzing contingency tables

Volker Roth, Edgar Dahl, Peter J. Wild, 2009, ICML '09.

The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data

Volker Roth, Thomas J. Fuchs, Julia E. Vogt, 2010, ICML.

DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope

S. Joseph Sirintrapun, Thomas J. Fuchs, Peter J. Schüffler, 2017 .

VOCA: Cell Nuclei Detection In Histopathology Images By Vector Oriented Confidence Accumulation

Thomas J. Fuchs, Chensu Xie, Chad M. Vanderbilt, 2018, MIDL.

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

Yumi Iwashita, Michael S. Ryoo, Thomas J. Fuchs, 2013, BMVC.

Understanding Neural Networks Through Deep Visualization pdf

Hod Lipson, Jason Yosinski, Jeff Clune, 2015, ArXiv.

MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing

Ahmet Zehir, Thomas J. Fuchs, Shweta S. Chavan, 2020 .

Feature selection strategies for classifying high dimensional astronomical data sets

S. G. Djorgovski, Ciro Donalek, Matthew J. Graham, 2013, 2013 IEEE International Conference on Big Data.

Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning

Thomas J. Fuchs, Dig Vijay Kumar Yarlagadda, Chensu Xie, 2020, MIDL.

Computational Pathology: A Machine Learning Approach

Thomas J. Fuchs, 2010 .

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

Thomas J. Fuchs, David Joon Ho, Chad M. Vanderbilt, 2020, MICCAI.

Boosting Convolutional Features for Robust Object Proposals pdf

Stefano Soatto, Thomas J. Fuchs, Nikolaos Karianakis, 2015, ArXiv.

Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma

Joachim M. Buhmann, Volker Roth, Cheng Soon Ong, 2013, Similarity-Based Pattern Analysis and Recognition.

Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2008, DAGM-Symposium.

Automatic and robust forearm segmentation using graph cuts

Gábor Székely, Matthias Harders, Thomas J. Fuchs, 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data

Joachim M. Buhmann, Thomas J. Fuchs, Verena Kaynig, 2010, MICCAI.

DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope

Andrew J. Schaumberg, Thomas J. Fuchs, Peter J. Schüffler, 2016, bioRxiv.

Sparse meta-Gaussian information bottleneck

Volker Roth, Thomas J. Fuchs, Mélanie Rey, 2014, ICML.

End-to-end dexterous manipulation with deliberate interactive estimation

Larry H. Matthies, Joel W. Burdick, Abhinandan Jain, 2012, 2012 IEEE International Conference on Robotics and Automation.

Inter-active learning of randomized tree ensembles for object detection

Joachim M. Buhmann, Thomas J. Fuchs, 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology pdf

Thomas J. Fuchs, Gabriele Campanella, Vitor Werneck Krauss Silva, 2018, ArXiv.

Neuron geometry extraction by perceptual grouping in ssTEM images

Joachim M. Buhmann, Thomas J. Fuchs, Verena Kaynig, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma

Joachim M. Buhmann, Volker Roth, Cheng Soon Ong, 2010, DAGM-Symposium.

Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue

Joachim M. Buhmann, Thomas J. Fuchs, Holger Moch, 2009, MICCAI.

TMARKER: A free software toolkit for histopathological cell counting and staining estimation

Joachim M. Buhmann, Cheng Soon Ong, Peter J. Wild, 2013, Journal of pathology informatics.

A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes

Thomas J. Fuchs, Lyndon D. Estes, Kelly K. Caylor, 2016 .

Autonomous Terrain Classification With Co- and Self-Training Approach

Takashi Kubota, Masahiro Ono, Thomas J. Fuchs, 2016, IEEE Robotics and Automation Letters.

Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology

Yukako Yagi, Thomas J. Fuchs, Peter J. Schüffler, 2017, Comput. Medical Imaging Graph..

Large-Scale Annotation of Histopathology Images from Social Media

Bin Xu, Andrew J. Schaumberg, Wendy C. Juarez-Nicanor, 2018, bioRxiv.

Guided review by frequent itemset mining: additional evidence for plaque detection

Philippe C. Cattin, Gábor Székely, Thomas J. Fuchs, 2009, International Journal of Computer Assisted Radiology and Surgery.

Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys

Ciro Donalek, Matthew J. Graham, Ashish Mahabal, 2014, 2014 IEEE 10th International Conference on e-Science.

Multi-Organ Cancer Classification and Survival Analysis pdf

Stefan Bauer, Joachim M. Buhmann, Peter J. Wild, 2016, ArXiv.

Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder

Michalis Doukas, Thomas J. Fuchs, Thomas Börner, 2019, MICCAI.

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Andrew J. Schaumberg, Wendy C. Juarez-Nicanor, Sarah J. Choudhury, 2020, Modern Pathology.

Computational Pathology: Challenges and Promises for Tissue Analysis

Joachim M. Buhmann, Thomas J. Fuchs, J. Buhmann, 2015, Comput. Medical Imaging Graph..

Structure Preserving Embedding of Dissimilarity Data

Joachim M. Buhmann, Volker Roth, Thomas J. Fuchs, 2013, Similarity-Based Pattern Analysis and Recognition.

Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary pdf

Michalis Doukas, Thomas J. Fuchs, Thomas Börner, 2019, ArXiv.

Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations pdf

Thomas J. Fuchs, Peter J. Schüffler, Ed Reznik, 2016, MLHC.

Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation

Thomas J. Fuchs, David Joon Ho, Anne Grabenstetter, 2019, Comput. Medical Imaging Graph..

Real-time data mining of massive data streams from synoptic sky surveys

Ciro Donalek, Matthew J. Graham, Ashish Mahabal, 2016, Future Gener. Comput. Syst..

DeepRec: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem pdf

Thomas J. Fuchs, Gabriele Campanella, Ida Haeggstroem, 2018, ArXiv.

Early Recognition of Human Activities from First-Person Videos Using Onset Representations pdf

Jake K. Aggarwal, Larry H. Matthies, Michael S. Ryoo, 2014, ArXiv.

Combined shape, appearance and silhouette for simultaneous manipulator and object tracking

Joel W. Burdick, Thomas J. Fuchs, Nicolas Hudson, 2012, 2012 IEEE International Conference on Robotics and Automation.

Randomized Tree Ensembles for Object Detection in Computational Pathology

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2009, ISVC.

Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me?

Jake K. Aggarwal, Larry H. Matthies, Michael S. Ryoo, 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

Joachim M. Buhmann, Volker Roth, Edgar Dahl, 2010, BMC Bioinformatics.

Survival Modeling of Pancreatic Cancer with Radiology Using Convolutional Neural Networks

Ida Häggström, Thomas J. Fuchs, Hassan Muhammad, 2018, POCUS/BIVPCS/CuRIOUS/CPM@MICCAI.

Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2008, MICCAI.

DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem

Thomas J. Fuchs, C. Ross Schmidtlein, Gabriele Campanella, 2018, Medical Image Anal..

Quickly Boosting Decision Trees - Pruning Underachieving Features Early

Pietro Perona, Thomas J. Fuchs, Piotr Dollár, 2013, ICML.

EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting pdf

Thomas J. Fuchs, Michael Doukas, Carlie S. Sigel, 2021, ArXiv.

FlexTileSource: An OpenSeadragon Extension for Efficient Whole-Slide Image Visualization

Thomas J. Fuchs, Peter J. Schüffler, Gamze Gokturk Ozcan, 2021, Journal of pathology informatics.

EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, with Prognostic Stratification Boosting

Thomas J. Fuchs, Amber L. Simpson, Michalis Doukas, 2021, MIDL.

Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation

Thomas J. Fuchs, David Joon Ho, Anne Grabenstetter, 2019, Comput. Medical Imaging Graph..

Nuc2Vec: Learning Representations of Nuclei in Histopathology Images with Contrastive Loss

Thomas J. Fuchs, Chad M. Vanderbilt, Chao Feng, 2021, MIDL.

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects

Thomas J. Fuchs, D. Hood, R. Ritch, 2017, Journal of glaucoma.

D R A F T H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer

Andrew J. Schaumberg, Thomas J. Fuchs, M. Rubin, 2017 .

A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles

Thomas J. Fuchs, Nicola Zamboni, Uwe Sauer, 2011, Metabolomics.

TAK1 suppresses a NEMO-dependent but NF-kappaB-independent pathway to liver cancer.

Thomas J. Fuchs, Thomas Fuchs, Stefan Zoller, 2010, Cancer cell.

A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts

Joachim M. Buhmann, Volker Roth, Peter J. Wild, 2012, PloS one.

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies

Leo Grady, Victor Reuter, Thomas J. Fuchs, 2020, Modern Pathology.

Semi-automatic assessment of the terminal ileum and colon in Crohn disease patients using MRI ( the VIGOR + + project )

Thomas J. Fuchs, Stuart A. Taylor, A. Forbes, 2018 .

Prediction Rules for the Detection of Coronary Artery Plaques: Evidence From Cardiac CT

Philippe C. Cattin, Gábor Székely, Thomas J. Fuchs, 2009, Investigative radiology.

A DEEP LEARNING ALGORITHM WITH HIGH SENSITIVITY FOR THE DETECTION OF BASAL CELL CARCINOMA IN MOHS SURGERY FROZEN SECTIONS

Thomas J. Fuchs, A. Rossi, K. Nehal, 2020, Journal of the American Academy of Dermatology.

PATHOLOGY INFORMATICS SUMMIT 2018

David E. Breen, William B. Lober, Hamid R. Tizhoosh, 2018, Journal of Pathology Informatics.

Title: Interpretable deep learning of myelin histopathology in age-related cognitive impairment Running Title: Deep learning of histopathology in cognitive impairment

Thomas J. Fuchs, J. Attems, A. McKee, 2022 .

Interpretable deep learning of myelin histopathology in age-related cognitive impairment

Thomas J. Fuchs, M. Mesulam, T. Montine, 2022, bioRxiv.

Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project).

Thomas J. Fuchs, Stuart A. Taylor, J. Buhmann, 2018, Academic radiology.

Deep conditional transformation models for survival analysis

Thomas J. Fuchs, T. Hothorn, Gabriele Campanella, 2022, ArXiv.

Machine learning approaches based on fibroblast morphometry confidently identify stress but have limited ability to predict ALS

Thomas J. Fuchs, G. Manfredi, Csaba Konrád, 2022, bioRxiv.

Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys

Thomas J. Fuchs, S. Djorgovski, A. Mahabal, 2014, 2014 IEEE 10th International Conference on e-Science.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Thomas J. Fuchs, Allen P. Miraflor, Matthew G Hanna, 2019, Nature Medicine.

Independent real‐world application of a clinical‐grade automated prostate cancer detection system

Thomas J. Fuchs, Christopher Kanan, S. Chandarlapaty, 2021, The Journal of pathology.

Computational biomarker predicts lung ICI response via deep learning-driven hierarchical spatial modelling from H&E

Thomas J. Fuchs, Chen-Wei Xie, M. Hellmann, 2022 .

A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles

Thomas J. Fuchs, J. Buhmann, U. Sauer, 2012, Metabolomics.

Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.

Thomas J. Fuchs, A. Halpern, C. Longo, 2021, The Journal of investigative dermatology.

Detection of Urothelial Bladder Cancer Cells in Voided Urine Can Be Improved by a Combination of Cytology and Standardized Microsatellite Analysis

Volker Roth, Thomas J. Fuchs, Joachim M Buhmann, 2009, Cancer Epidemiology Biomarkers & Prevention.

Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

Thomas J. Fuchs, M. Robson, Jin Roh, 2022, Journal of pathology informatics.

Whole slide imaging equivalency and efficiency study: experience at a large academic center

Thomas J. Fuchs, Thomas Fuchs, Yukako Yagi, 2019, Modern Pathology.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Thomas J. Fuchs, Allen P. Miraflor, V. Reuter, 2019, Nature Medicine.

Effect of reader experience on variability, evaluation time and accuracy of coronary plaque detection with computed tomography coronary angiography

Gábor Székely, Thomas J. Fuchs, Hatem Alkadhi, 2010, European Radiology.

Machine Learning Approaches Based on Fibroblast Morphometry Do Not Predict ALS

Thomas J. Fuchs, G. Manfredi, Csaba Konrád, 2023, SSRN Electronic Journal.

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

Thomas J. Fuchs, Melissa P. Murray, Y. Yagi, 2021, J. Am. Medical Informatics Assoc..

Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Thomas J. Fuchs, Narasimhan P. Agaram, M. Hameed, 2022, The American journal of pathology.

The acute coronary syndrome--pre-hospital diagnostic quality.

Thomas J. Fuchs, J. Smolle, G. Gemes, 2005, Resuscitation.

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

Thomas J. Fuchs, Christopher Kanan, J. Reis-Filho, 2022, Archives of pathology & laboratory medicine.

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Arkadiusz Gertych, Thomas J. Fuchs, Nathan Ing, 2015, Comput. Medical Imaging Graph..

Overcoming an Annotation Hurdle: Digitizing Pen Annotations from Whole Slide Images

Thomas J. Fuchs, P. Schüffler, C. Vanderbilt, 2021, Journal of pathology informatics.

Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.

Thomas J. Fuchs, Christopher Kanan, S. Chandarlapaty, 2020 .

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Thomas J. Fuchs, M. Amin, B. Knudsen, 2015, Comput. Medical Imaging Graph..