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Thomas Fuchs

  1. "MSK Grad Students"
Formats
  1. Journal Article (6)
  2. Conference Paper (5)
  3. Research Letter (1)
Groups
  1. MSK Grad Students (12)
  2. Medical Physics (12)
  3. Department of Pathology and Laboratory Medicine (7)
  4. Sloan Kettering Institute Postdocs (7)
  5. Center for Pancreatic Cancer Research (3)
  6. Melanoma Disease Management Team (3)

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People
  1. Gabriele Campanella (7)
  2. Hassan Muhammad (4)
  3. Andrew Schaumberg (2)
  4. Chad Vanderbilt (2)
  5. Chensu Xie (2)
  6. David Klimstra (2)

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Years
  1. 2022 (1)
  2. 2021 (1)
  3. 2020 (2)
  4. 2019 (4)
  5. 2018 (2)
  6. 2017 (2)
Publications
  1. Lecture Notes in Computer Science (3)
  2. Proceedings of Machine Learning Research (2)
  3. Computerized Medical Imaging and Graphics (1)
  4. Journal of Glaucoma (1)
  5. Journal of Investigative Dermatology (1)
  6. Journal of the American Academy of Dermatology (1)

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Publishers
  1. Springer (3)
  2. Elsevier Science, Inc. (2)
  3. Journal Machine Learning Research (2)
  4. Elsevier Inc. (1)
  5. Lippincott Williams & Wilkins (1)
  6. Mosby Elsevier (1)

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Results (12)

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  • Deep learning for basal cell carcinoma detection for reflectance confocal microscopy
    Journal Article

    Campanella, G.; Navarrete-Dechent, C.; Liopyris, K.; Monnier, J.; Aleissa, S.; Minhas, B.; Scope, A.; Longo, C.; Guitera, P.; Pellacani, G.; more...
    Journal of Investigative Dermatology (2022)

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  • A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections
    Research Letter

    Campanella, G.; Nehal, K. S.; Lee, E. H.; Rossi, A.; Possum, B.; Manuel, G.; Fuchs, T. J.; Busam, K. J.
    Journal of the American Academy of Dermatology (2021)

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  • Beyond classification: Whole slide tissue histopathology analysis by end-to-end part learning
    Conference Paper

    Xie, C.; Muhammad, H.; Vanderbilt, C. M.; Caso, R.; Yarlagadda, D. V. K.; Campanella, G.; Fuchs, T. J.
    Journal Machine Learning Research (2020)

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

    Schaumberg, A. J.; Juarez-Nicanor, W. C.; Choudhury, S. J.; Pastrián, L. G.; Pritt, B. S.; Prieto Pozuelo, M.; Sotillo Sánchez, R.; Ho, K.; Zahra, N.; Sener, B. D.; more...
    Modern Pathology (2020)

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  • VOCA: Cell nuclei detection in histopathology images by vector oriented confidence accumulation
    Conference Paper

    Xie, C.; Vanderbilt, C. M.; Grabenstetter, A.; Fuchs, T. J.
    Journal Machine Learning Research (2019)

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  • Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder
    Conference Paper

    Muhammad, H.; Sigel, C. S.; Campanella, G.; Boerner, T.; Pak, L. M.; Büttner, S.; Ijzermans, J. N. M.; Koerkamp, B. G.; Doukas, M.; Jarnagin, W. R.; more...
    Springer (2019)

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

    Campanella, G.; Hanna, M. G.; Geneslaw, L.; Miraflor, A.; Werneck Krauss Silva, V.; Busam, K. J.; Brogi, E.; Reuter, V. E.; Klimstra, D. S.; Fuchs, T. J.
    Nature Medicine (2019)

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  • DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem
    Journal Article

    Häggström, I.; Schmidtlein, C. R.; Campanella, G.; Fuchs, T. J.
    Medical Image Analysis (2019)

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  • Survival modeling of pancreatic cancer with radiology using convolutional neural networks
    Conference Paper

    Muhammad, H.; Häggström, I.; Klimstra, D. S.; Fuchs, T. J.
    Springer (2018)

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

    Campanella, G.; Rajanna, A. R.; Corsale, L.; Schüffler, P. J.; Yagi, Y.; Fuchs, T. J.
    Computerized Medical Imaging and Graphics (2018)

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