# A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification

arXiv: https://arxiv.org/abs/2007.15897 · HTML: https://mlsanity.com/paper/2007.15897/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Local Multi-Head Channel Self-Attention for Facial Expression Recognition](https://mlsanity.com/paper/2111.07224/) (2021)
- [Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction](https://mlsanity.com/paper/2105.01456/) (2021)
- [Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis](https://mlsanity.com/paper/2106.12313/) (2021)
- [Multi Head Attention Enhanced Inception v3 for Cardiomegaly Detection](https://mlsanity.com/paper/2511.20101/) (2025)

## Abstract (arXiv)

Spatial attention has been introduced to convolutional neural networks (CNNs) for improving both their performance and interpretability in visual tasks including image classification. The essence of the spatial attention is to learn a weight map which represents the relative importance of activations within the same layer or channel. All existing attention mechanisms are local attentions in the sense that weight maps are image-specific. However, in the medical field, there are cases that all the images should share the same weight map because the set of images record the same kind of symptom related to the same object and thereby share the same structural content. In this paper, we thus propose a novel global spatial attention mechanism in CNNs mainly for medical image classification. The global weight map is instantiated by a decision boundary between important pixels and unimportant pixels. And we propose to realize the decision boundary by a binary classifier in which the intensities of all images at a pixel are the features of the pixel. The binary classification is integrated into an image classification CNN and is to be optimized together with the CNN. Experiments on two medical image datasets and one facial expression dataset showed that with the proposed attention, not only the performance of four powerful CNNs which are GoogleNet, VGG, ResNet, and DenseNet can be improved, but also meaningful attended regions can be obtained, which is beneficial for understanding the content of images of a domain.