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ISSN -
Print: 2229-5089, Online: 2153-3539
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 Latest
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| Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data |
| Hooman H Rashidi, Imran H Khan, Luke T Dang, Samer Albahra, Ujjwal Ratan, Nihir Chadderwala, Wilson To, Prathima Srinivas, Jeffery Wajda, Nam K Tran |
| High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We soug... |
| J Pathol Inform 2022,
13:10 (19 January 2022) |
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[Abstract] | [HTML Full text] | [PDF] | [Mobile HTML Full text] | [EPub] |
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| Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series |
| Dmitrii Bychkov, Heikki Joensuu, Stig Nordling, Aleksei Tiulpin, Hakan Kücükel, Mikael Lundin, Harri Sihto, Jorma Isola, Tiina Lehtimäki, Pirkko-Liisa Kellokumpu-Lehtinen, Karl von Smitten, Johan Lundin, Nina Linder |
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Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this ... |
| J Pathol Inform 2022,
13:9 (18 January 2022) |
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[Abstract] | [HTML Full text] | [PDF] | [Mobile HTML Full text] | [EPub] |
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| Measuring digital pathology throughput and tissue dropouts |
| George L Mutter, David S Milstone, David H Hwang, Stephanie Siegmund, Alexander Bruce |
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Background: Digital pathology operations that precede viewing by a pathologist have a substantial impact on costs and fidelity of the digital image. Scan time and file size determine throughp... |
| J Pathol Inform 2022,
13:8 (08 January 2022) |
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[Abstract] | [HTML Full text] | [PDF] | [Mobile HTML Full text] | [EPub] |
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