Paper 13407-52
Hyperspectral masked autoencoder for tissue reconstruction and downstream tasks
19 February 2025 • 5:30 PM - 7:00 PM PST | Golden State Ballroom
Abstract
The pre-training of transformer architectures has been shown to improve deep learning networks in various tasks including natural language processing and computer vision. While this approach has shown promise in various fields, more development and translation need to be dedicated to medical imaging applications. Current literature scarcely focuses on thorough assessment of implemented pre-training approaches as well, potentially hindering performance in downstream tasks. In this work we leverage a state-of-the-art pre-training architecture with hyperspectral imaging (HSI) to effectively encode spatial and spectral features of various ex vivo tissues. We utilize a masked autoencoding scheme to perform pre-training on an internal dataset captured with a high-speed hyperspectral laparoscopic imaging system. Pre-training results are qualitatively assessed through reconstruction visualization and quantitatively assessed with mean squared error.
Presenter
The Univ. of Texas at Dallas (United States)
I am a third year PhD at the University of Texas at Dallas in the Bioengineering department. Under Dr. Fei, my research interests are hyperspectral imaging applications with a focus on laparoscopic procedures.