Abstract #1882
Semi-Supervised Learning with Spatial Pseudo Labeling for Peritumoral Infiltration Prediction in Glioblastoma using MR Fingerprinting
Walter Zhao1, Xiaofeng Wang2, Charit Tippareddy3, Hamed Akbari4,5, Anahita Fathi Kazerooni4,5, Christos Davatzikos4,5, Marta Couce6, Andrew E. Sloan6,7,8,9, Chaitra Badve3, and Dan Ma1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States, 3Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 4Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 5Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 6Department of Pathology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 7Department of Neurosurgery, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 8Seidman Cancer Center and Case Comprehensive Cancer Center, Cleveland, OH, United States, 9Piedmont Health, Atlanta, GA, United States
Synopsis
Keywords: Machine Learning/Artificial Intelligence, CancerExisting glioblastoma (GB) infiltration models are often limited by lack of true infiltration labels and employ the assumption that edema closer to tumor has higher infiltrative potential relative to distant edema. Here, we propose a semi-supervised learning scheme that incorporates pretraining on the near-far heuristic and spatial pseudo labeling using true infiltration labels for voxel-wise tumor infiltration prediction. Our results show improved classification performance following finetuning on labeled infiltration data compared to training on the near-far heuristic alone and indicate the potential in employing MR fingerprinting-based models to guide GB diagnosis and treatment.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
