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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">This is an announcement of
<span style="letter-spacing:.15pt;background:white">Renyu Zhang’s Dissertation Defense</span><br>
===============================================<br>
<b>Candidate: </b><span style="letter-spacing:.15pt;background:white">Renyu Zhang’s</span><br>
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<b>Date:</b> Monday, May 13th, 2024<br>
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<b>Time:</b> 2:30 pm CT<br>
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<b>Location:</b> JCL 298<br>
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<b>Title:</b> </span><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black;letter-spacing:.15pt;background:white;mso-ligatures:none">Machine Learning for Histopathology Images in Low-data Regime</span><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="letter-spacing:.15pt;background:white">Abstract:</span></b><span style="letter-spacing:.15pt;background:white"> 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, leading to a caption generation model from histopathology images, a hyperbolic attention model for histopathology images, a deep Bayesian active learning method to enable efficient selection of training examples that can undergo
expensive annotation, and representation learning approach that utilize existing coarse-grained labels of whole slide images to improve model performance on fine-grained data. Our experiments demonstrate that these approaches can improve the performances of
models in the low-data regime while maintaining high levels of interpretability, minimizing labeling costs, and showing analytical advantages. The results of this study provide valuable insights for future research in the area of machine learning in low-data
regimes for histopathology images.</span><span style="letter-spacing:.15pt"><br>
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<b><span style="background:white">Advisors:</span></b><span style="background:white"> Robert L. Grossman</span><br>
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<b><span style="background:white">Committee Members:</span></b><span style="background:white"> Robert L. Grossman, Aly A. Khan, and Yuxin Chen</span></span><o:p></o:p></span></p>
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