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<div dir="ltr"><span class="x_ContentPasted0" style="text-decoration: none; box-sizing: content-box; margin: 0px; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255);">This is an announcement of Renyu Zhang’s
Candidacy Exam.</span>
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<div style="box-sizing:content-box;margin:0px">===============================================</div>
<div style="box-sizing:content-box;margin:0px">Candidate: Renyu Zhang</div>
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Date: Monday, June 26, 2023</div>
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Time: 1:30 pm CST</div>
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Location: JCL 298</div>
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Title: Machine Learning for Biomedical Images in Low-Data Regime</div>
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Abstract: Diagnostic pathology and histopathology images play a critical role in the diagnosis and treatment of carcinomas. In order to get satisfying performance, we usually need a large amount of labeled data. Annotating a large number of histopathology images
for training machine learning models can be expensive and time-consuming. We explored several approaches of machine learning in a low-data regime for histopathology images, including caption generation from histology images [Zhang et al., 2020b], a deep Bayesian
active learning method [Zhang et al., 2023] to enable efficient selection of training examples that can undergo more expensive annotation, and representation learning approach that utilize existing coarse-grained labels for H&E slides to improve model performance
on fine-grained data. Our experiments demonstrate that these approaches can improve the accuracy of models in the low-data regime while maintaining high levels of interpretability. The results of this study provide valuable insights for future research in
the area of machine learning in low-data regimes for histopathology images.</div>
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Advisors: Robert L. Grossman</div>
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Committee Members: Robert L. Grossman, Aly A. Khan, and Yuxin Chen</div>
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<span style="text-decoration: none; box-sizing: content-box; margin: 0px; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; color: rgb(33, 33, 33);"><a href="https://urldefense.com/v3/__https://drive.google.com/file/d/15ftMGuxg6-fRYVNfObLnpOzCc-C9jdRw/view?usp=drive_web__;!!BpyFHLRN4TMTrA!51WmbVugZWrpvj2edkSQUTTk7ZmvNv-zbL9hQFdLXTFoGZXsU9ESBq9lX2vICpc_XqNPwh9Asc94w4RwQjseBRpr6Gc$" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="box-sizing: content-box; margin: 0px;"> CandidacyProposal.pdf</a></span><br class="Apple-interchange-newline" style="text-decoration:none">
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