Stroke lesion localization in 3D MRI datasets with deep reinforcement learning
In person: 22 February 2022 • 11:30 AM - 11:50 AM PST
The efficacy of stroke treatments is highly time-sensitive, and accelerated diagnosis may improve patient outcomes. Lesion identification in MRI datasets is time consuming and challenging. Automatic lesion localization can expedite diagnosis by flagging images and corresponding regions of interest for visual assessment. In this work, we propose a deep reinforcement learning model to localize and detect ischemic stroke lesions in fluid attenuated inversion recovery MRI images, combining advances in computer vision to sequentially localize multiple lesions. The results show that the model learns to successfully localize lesions in challenging hybrid data from multiple studies.
Univ. of Calgary (Canada)
Sam is a computer science student at the University of Calgary, where he works with the Medical Image Processing and Machine Learning Lab. His primary research interests are in machine learning and deep learning, particularly at the intersection of deep learning with techniques from reinforcement learning. He has experience developing computational models for developmental biology and medical imaging applications.