DeepAI AI Chat
Log In Sign Up

Multi-scale guided attention for medical image segmentation

by   Ashish Sinha, et al.

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in non-optimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to remove the noise and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of abdominal organ segmentation on magnetic resonance imaging (MRI). A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code and the trained model are made publicly available at:


page 1

page 8

page 9


MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation

The UNet architecture, based on Convolutional Neural Networks (CNN), has...

MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Although convolutional neural networks (CNNs) are promoting the developm...

Channel prior convolutional attention for medical image segmentation

Characteristics such as low contrast and significant organ shape variati...

Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation

Despite the growing success of Convolution neural networks (CNN) in the ...

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

Recent progress in biomedical image segmentation based on deep convoluti...

Studying the Effects of Self-Attention for Medical Image Analysis

When the trained physician interprets medical images, they understand th...

Multi-level feature fusion network combining attention mechanisms for polyp segmentation

Clinically, automated polyp segmentation techniques have the potential t...

Code Repositories


Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

view repo


Medical Image Computation, Analysis, and Learning

view repo