Machine Learning in Medicine (MLxMed) Seminar March 22
A Virtual Seminar Series
Hosted by the Department of Biomedical Informatics, University of Pittsburgh; the Hariri Institute for Computing, Boston University; and the University of Toronto
Speaker: Osmar R. Zaïane, MS, PhD, Professor, Computing Science, University of Alberta
Talk Title:Automatic Low-quality Fundus Image Enhancement and Diabetic Retinopathy Grading with Explanations
Date: Friday, March 22, 2024
Time: 2:00 PM – 3:00 PM Eastern Time
Zoom https://pitt.zoom.us/j/96092195623
(Details are listed at the end)
ABSTRACT
We present two lines of work that are connected but not yet put together, namely the automatic enhancement of retinal image quality and the classification of retinal images.
Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy or Diabetic Macular Edema. However, low-quality fundus images potentially increase uncertainty in the diagnosis of eye fundus disease and even lead to misdiagnosis by ophthalmologists. We explore the potential of a self-supervised task for improving the quality of fundus images without the requirement of high-quality reference images.
Traditional Diabetic Retinopathy (DR) automatic classification algorithms rely on the precise detection of microaneurysms and hemorrhage lesions. Such lesion annotation is an expensive and time-consuming process and therefore it is expected to develop automatic grading methods with only image-level annotations. We formulate the weakly supervised DR grading as a multi-instance learning problem and propose a domain adaptation multi-instance learning with attention mechanism for DR grading.
About MLxMed Seminar Series
Medicine is complex and data-driven, while discovery and decision-making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will feature prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support. For more information, see MLxMed website
Zoom Information
When: Friday, March 22, 2024, 2:00 PM Eastern Time (US and Canada)
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