Publications of Mark E. Sherman

all all years 2022 2020 2019 2018 2017

Papers in international journals

  1. C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. Bokhorst, J. van der Laak and F. Ciompi, "Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer.", NPJ breast cancer, 2022;8(1):120.
    Abstract DOI PMID Cited by ~9
  2. J. Ogony, T. de Bel, D. Radisky, J. Kachergus, E. Thompson, A. Degnim, K. Ruddy, T. Hilton, M. Stallings-Mann, C. Vachon, T. Hoskin, M. Heckman, R. Vierkant, L. White, R. Moore, J. Carter, M. Jensen, L. Pacheco-Spann, J. Henry, A. Storniolo, S. Winham, J. van der Laak and M. Sherman, "Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence", Breast Cancer Research, 2022;24.
    Abstract DOI PMID Cited by ~1
  3. M. Sherman, T. de Bel, M. Heckman, L. White, J. Ogony, M. Stallings-Mann, T. Hilton, A. Degnim, R. Vierkant, T. Hoskin, M. Jensen, L. Pacheco-Spann, J. Henry, A. Storniolo, J. Carter, S. Winham, D. Radisky and J. van der Laak, "Serum hormone levels and normal breast histology among premenopausal women", Breast Cancer Research and Treatment, 2022;194:149-158.
    Abstract DOI PMID
  4. T. de Bel, G. Litjens, J. Ogony, M. Stallings-Mann, J. Carter, T. Hilton, D. Radisky, R. Vierkant, B. Broderick, T. Hoskin, S. Winham, M. Frost, D. Visscher, T. Allers, A. Degnim, M. Sherman and J. van der Laak, "Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning", npj Breast Cancer, 2022;8.
    Abstract DOI PMID Cited by ~5
  5. M. Mullooly, B. Ehteshami Bejnordi, R. Pfeiffer, S. Fan, M. Palakal, M. Hada, P. Vacek, D. Weaver, J. Shepherd, B. Fan, A. Mahmoudzadeh, J. Wang, S. Malkov, J. Johnson, S. Herschorn, B. Sprague, S. Hewitt, L. Brinton, N. Karssemeijer, J. van der Laak, A. Beck, M. Sherman and G. Gierach, "Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density", NPJ Breast Cancer, 2019;5:43.
    Abstract DOI PMID Download Cited by ~14
  6. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, M. Sherman, A. Polonia, J. Parry, M. Abubakar, G. Litjens, J. van der Laak and F. Ciompi, "Learning to detect lymphocytes in immunohistochemistry with deep learning", Medical Image Analysis, 2019;58:101547.
    Abstract DOI PMID Cited by ~106
  7. B. Ehteshami Bejnordi, M. Mullooly, R. Pfeiffer, S. Fan, P. Vacek, D. Weaver, S. Herschorn, L. Brinton, B. van Ginneken, N. Karssemeijer, A. Beck, G. Gierach, J. van der Laak and M. Sherman, "Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies", Modern Pathology, 2018;31(10):1502-1512.
    Abstract DOI PMID Cited by ~150

Preprints

  1. C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. van der Laak and F. Ciompi, "Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer", arXiv:2012.04974, 2020.
    Abstract DOI arXiv Cited by ~1

Papers in conference proceedings

  1. B. Bejnordi, J. Lin, B. Glass, M. Mullooly, G. Gierach, M. Sherman, N. Karssemeijer, J. van der Laak and A. Beck, "Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images", IEEE International Symposium on Biomedical Imaging, 2017:929-932.
    Abstract DOI PMID arXiv Cited by ~63