Zobrazeno 1 - 7 of 7 pro vyhledávání: '"Kenneth, Shroyer"'
1
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Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
Autor: Abousamra, Shahira, Hou, Le, Gupta, Rajarsi, Chen, Chao, Samaras, Dimitris, Kurc, Tahsin, Batiste, Rebecca, Zhao, Tianhao, Kenneth, Shroyer, Saltz, Joel
Deep learning classifiers for characterization of whole slide tissue morphology require large volumes of annotated data to learn variations across different tissue and cancer types. As is well known, manual generation of digital pathology training da
Externí odkaz: http://arxiv.org/abs/1907.03960
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2
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Label Super Resolution with Inter-Instance Loss
Autor: Zhao, Maozheng, Hou, Le, Le, Han, Samaras, Dimitris, Jojic, Nebojsa, Fassler, Danielle, Kurc, Tahsin, Gupta, Rajarsi, Malkin, Kolya, Kenneth, Shroyer, Saltz, Joel
For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology images. On the other hand, collecting low resolution labels (labels for
Externí odkaz: http://arxiv.org/abs/1904.04429
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6
Keratin 17: Cervical Cancer Prognostic Marker Promotes p27‐Nuclear Export and Tumor Growth
Autor: Elizabeth Vanner, Kenneth Shroyer, Luisa F. Escobar-Hoyos, Lucia Roa-Peña, Ali Akalin, Ruchi Shah
Publikováno v: The FASEB Journal. 29
Recently, we identified keratin 17 (K17) as a negative prognostic marker for cervical cancer and based on preliminary data, we set out to determine if K17 is related to tumor stage and sustained proliferation. K17 and tumor suppressor p27 were detect
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9681c0849c267560d06396db5f8d55f2
https://doi.org/10.1096/fasebj.29.1_supplement.lb448
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