# Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping

arXiv: https://arxiv.org/abs/2303.14901 · HTML: https://mlsanity.com/paper/2303.14901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Orthogonal Ensemble Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.10827/) (2021)
- [Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification](https://mlsanity.com/paper/2101.10667/) (2021)
- [Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients](https://mlsanity.com/paper/2205.10838/) (2022)
- [Lung infection and normal region segmentation from CT volumes of COVID-19 cases](https://mlsanity.com/paper/2201.03050/) (2022)

## Abstract (arXiv)

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volumes, however, because of the heavy workload, an automatic analysis method using a computer is desired. Most computer-aided diagnosis studies have addressed only a portion of the elements necessary for the identification. In this work, we realize the identification method through a classification task by using a 2.5-dimensional CNN with three-dimensional attention mechanisms. We visualize the suspicious regions by applying a backpropagation based on positive gradients to attention-weighted features. We perform experiments on an in-house dataset and two public datasets to reveal the generalization ability of the proposed method. The proposed architecture achieved AUCs of over 0.900 for all the datasets, and mean sensitivity 0.853 ± 0.036 and specificity 0.870 ± 0.040. The method can also identify notable lesions pointed out in the radiology report as suspicious regions.