Drs. Gurcan and Fauzi are published in BMC Medical Informatics
and Decision Making with their paper Classification of follicular lymphoma: the
effect of computer aid on pathologists grading. Their paper presents a system, called Follicular Lymphoma
Grading System (FLAGS), to assist the pathologist in grading FL cases. The
results of this study show that FLAGS can be useful in increasing the
pathologists’ accuracy in grading the tissue. To the best of our knowledge,
this study measure, for the first time, the effect of computerized image
analysis on pathologists’ grading of follicular lymphoma. When fully developed,
such systems have the potential to reduce sampling bias by examining an
increased proportion of HPFs within follicle regions, as well as to reduce
inter- and intra-reader variability.
The CIA lab has recently had 4 articles published in PLOS One and the Journal of Urology. Automated Staging Of T1 Bladder Cancer Using Digital Pathologic H&E Images: A Deep Learning approach (Journal of Urology). The paper discusses the need for accurately gauging tumor cell intrusion into Lamina Propria in an effort to substage bladder cancer. It explains how transfer learning in conjunction with Convolutional Neural Networks can be used to accurately identify different bladder layers and then compute the distance between tumor nuclei and Lamina Propria. The article is available here: https://www.jurology.com/article/S0022-5347(18)41148-2/pdf Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning (PLOS One). This paper examines a proposed methodology to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. It also uses transfer learning to exploit a rich set of features ...
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ReplyDeleteGet the new and published notes in this section with the BMC medical informatics and enjoy the link of decisions making for the better life. You can click this and see the Drs. Gurcan with the new zone of the decision making.
ReplyDeleteLymphoma is not a usual problem as this causes a lot of problems as well because all the patients do explore the details of what has happened with the patients and you need to provide the best treatment you need.
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