Test Time Augmentation (TTA) is a simple yet powerful technique to improve image segmentation results by applying multiple transformations to an input image during inference and combining the outputs. When paired with models like U-Net, it often boosts performance without retraining. But what if we could go one step further Continue Reading
medical image segmentation
[Paper Summary] EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
![[Paper Summary] EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://idiotdeveloper.com/wp-content/uploads/2024/09/Paper-Summary-EMCAD-Efficient-Multi-scale-Convolutional-Attention-Decoding-for-Medical-Image-Segmentation-1024x536.jpg)
This post will analyze the research paper “EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.” We will discuss the problems with existing medical image segmentation methods and how the given method (EMCAD) solves these issues. What is EMCAD? EMCAD is a newly developed efficient multi-scale convolutional attention decoder Continue Reading