“We propose a Cross-Slice Attention Module (CSAM) that considers the cross-slice information,” Alex tells us. “We mostly analyze the images in 2D, but we incorporate 3D information into the 2D feature maps. For volumetric segmentation, 3D methods don’t work that well when dealing with anisotropic images, and 2D methods don’t use volumetric information.” While the problem is not entirely solved, substantial progress has been made by incorporating cross-slice information into the segmentation process with minimal trainable parameters. The proposed 2.5D segmentation approach bridges the gap by encoding and decoding the images in 2D but using 3D information during the attention so that the 2D feature maps are enriched with crucial volumetric data. “There’s still room to improve,” he reveals. “For the current method, I mostly use global information during the attention. That’s the information within the entire volume, but I think in some cases, only more local information would be sufficient or even better. Maybe finding a way to use more localized information would further improve the performance.” This paper is actually a follow-up to Alex’s previously published journal paper, CAT-Net, which he says had some flaws, including requiring a large number of parameters for training. His passion for this work stems from the prospect of overcoming these challenges and contributing a more memoryefficient solution to a less-explored problem. “What we’re trying to do is called 2.5D segmentation, and that’s not something many people have done,” he tells us. “There are some works that focus on it, but all of them have drawbacks. Some methods have hyperparameters that need to be set, which we don’t want to, and others are pretty large in terms of memory.” To learn more about Alex’s work, visit Orals 3.1 [Paper 3] today at 14:00-15:45 (Naupaka). 5 DAILY WACV Saturday CSAM: A 2.5D Cross-Slice Attention Module
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