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Talk Title: Artificial Intelligence for Medical Image Analysis for Breast Cancer Multiparametric MRI

Watch Isabelle’s Spotlight Research Talk

Talk Abstract: Artificial intelligence is playing an increasingly important role in medical imaging. Computer-aided diagnosis (CADx) systems using human-engineered features or deep learning can potentially assist radiologists in image interpretation by extracting quantitative biomarkers to improve diagnostic performance and circumvent unnecessary invasive procedures. Multiparametric MRI (mpMRI) has become a part of routine clinical assessment for screening of high-risk patients for breast cancer and monitoring therapy response because it has been shown to improve diagnostic accuracy. Current CADx methods for breast lesion assessment on MRI, however, are mostly focused on one sequence, the dynamic contrast-enhanced (DCE)-MRI. Therefore, we investigated methods for incorporating three sequences in mpMRI to improve the CADx performance in differentiating benign and malignant breast lesions. We compared integrating the mpMRI information at the image level, feature level, or classifier output level. In addition, transfer learning is often employed in deep learning applications in medical imaging due to data scarcity. However, pretrained convolutional neural networks (CNNs) used in transfer learning require two-dimensional (2D) inputs, limiting the ability to utilize high-dimensional information in medical imaging. To address this problem, we investigated a transfer learning method that collapses volumetric information to 2D by taking the maximum intensity projection (MIP) at the feature level within CNNs, which outperformed a previous method of using MIPs of images themselves in the task of distinguishing between benign and malignant breast lesions. We proposed a method that combines feature fusion and feature MIP for computer-aided breast cancer diagnosis using high-dimensional mpMRI that outperforms the current benchmarks.

Bio: Isabelle is a PhD candidate in Medical Physics at the University of Chicago, supervised by Dr. Maryellen Giger. Her research is centered around developing automated methods for quantitative medical image analysis to assist in clinical decision-making. She has proposed novel methodologies to diagnoses breast cancer using multiparametric MRI exams. Since the pandemic, she has also been working on AI solutions that leverage medical images to enhance the early detection and prognosis of COVID-19. She has first-hand experience tackling unique challenges faced by medical imaging applications of machine learning due to high-dimensionality, data scarcity, noisy labels, etc. She loves working at the intersection of physics, medicine, and data science, and she is motivated by the profound potential impact that her research can bring on improving access to high-quality care and providing a proactive healthcare system. She hopes to dedicate her career to building AI-empowered technology to transform healthcare, accelerate scientific discoveries, and improving human well-being.