Search Results - "Yakir, Oz"

Adjuvant FOLFIRINOX in Patients with Resectable Pancreatic Cancer Is Effective but Rarely Feasible in Real Life: Is Neoadjuvant FOLFIRINOX a Better Option? by Maman, Yossi, Goykhman, Yaacov, Yakir, Oz, Barenboim, Alex, Geva, Ravit, Peles-Avraham, Sharon, Wolf, Ido, Klausner, Joseph M, Lahat, Guy, Lubezky, Nir

“…The recommended treatment for resectable pancreatic cancer (PC) is resection followed by adjuvant FOLFIRINOX. We assessed the proportion of patients that…”
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Dysglycemia in non-functioning pancreatic neuroendocrine tumors (NF-PNET): Further insights into an under recognized entity by Osher, Esther, Geva, Ravit, Wolf, Ido, Tordjman, Karen, Klausner, Joseph, Sofer, Yael, Scapa, Erez, Shibolet, Oren, Ben –Ami Shor, Dana, Bar-Yishay, Iddo, Lubezky, Nir, Goykhman, Yaacov, Lahat, Guy, Yakir, Oz, Pelles, Sharon, Aizic, Asaf, Blachar, Arye, Stern, Naftali, Greenman, Yona

“…•Pancreatic neuroendocrine tumors (PNETs) are rare, but their incidence has risen significantly in recent years.•Diabetes mellitus (DM) is clearly recognized…”
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A Lymph Node Ratio Model for Prognosis of Patients with Pancreatic Neuroendocrine Tumors by Osher, Esther, Shalabna, Eiman, Klausner, Joseph M, Greenman, Yona, Stern, Naftali, Shibolet, Oren, Scapa, Erez, Yakir, Oz, Shor, Dana Ben-Ami, Bar-Yishay, Iddo, Shamai, Sivan, Sofer, Yael, Lubezky, Nir, Goykhman, Yaacov, Lahat, Guy, Wolf, Ido, Pelles, Sharon, Aizic, Asaf, Blachar, Arye, Geva, Ravit

“…The objective of this study was to determine the prognostic value of lymph node (LN) involvement and the LN ratio (LNR) and their effect on recurrence rates…”
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Reconstructing Training Data From Real World Models Trained with Transfer Learning by Oz, Yakir, Yehudai, Gilad, Vardi, Gal, Antebi, Itai, Irani, Michal, Haim, Niv

“…Current methods for reconstructing training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution…”
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Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses by Buzaglo, Gon, Haim, Niv, Yehudai, Gilad, Vardi, Gal, Oz, Yakir, Nikankin, Yaniv, Irani, Michal

“…Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently,…”
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