S090 - Image Entropy and Numeric Representation for MRI Semantic Segmentation
Daniel A Di Giovanni, Pierrick Coupe, Danilo Bzdok, D. Louis Collins
Deep learning has made major strides in medical imaging segmentation in the last several years for its automated feature extraction. This model fitting process is susceptible to over-fitting, and can benefit from sparsity. Here, we show theoretical and experimental potential of using low-entropy images as sparse input to improve deep learning driven tissue segmentation, using tumor and heart segmentation problems as exemplary cases.
Schedule: Monday, July 10: Posters — 11:00–12:00 & 15:00–16:00
Poster location: M48