SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images

Abstract Background Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological information, which is vital for thoracic disease classification. Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions. Result We design a feature extractor equipped with a multi-scale attention module to learn global attention maps from global images. To exploit disease-specific cues effectively, we locate lung and heart regions containing pathological information by a well-trained pixel-wise segmentation model to generate binarization masks. By introducing element-wise logical AND operator on the learned global attention maps and the binarization masks, we obtain local attention maps in which pixels are are 1 for lung and heart region and 0 for other regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions. Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions. Our method with pixel-wise segmentation can help overcome the deviation of locating local regions. Evaluated by the benchmark split on the publicly available chest X-ray14 dataset, the comprehensive experiments show that our method achieves superior performance compared to the state-of-the-art methods. Conclusion We propose a novel deep framework for the multi-label classification of thoracic diseases in chest X-ray images. The proposed network aims to effectively exploit pathological regions containing the main cues for chest X-ray screening. Our proposed network has been used in clinic screening to assist the radiologists. Chest X-ray accounts for a significant proportion of radiological examinations. It is valuable to explore more methods for improving performance.

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COVID 19, Pneumonia and Other Disease Classification using Chest X-Ray images

2021 2nd International Conference for Emerging Technology (INCET) ◽

10.1109/incet51464.2021.9456192 ◽

2021 ◽

Author(s):

Abhishek Panwar ◽

Akshay Dagar ◽

Vishal Pal ◽

Vinod Kumar

Keyword(s):

Disease Classification ◽

X Ray ◽

Chest X Ray

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Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2018.8621107 ◽

2018 ◽

Cited By ~ 2

Author(s):

Chengsheng Mao ◽

Liang Yao ◽

Yiheng Pan ◽

Yuan Luo ◽

Zexian Zeng

Keyword(s):

Disease Diagnosis ◽

Thoracic Disease ◽

X Ray ◽

Chest X Ray

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Chest X-Ray Image Preprocessing for Disease Classification

Procedia Computer Science ◽

10.1016/j.procs.2021.08.068 ◽

2021 ◽

Vol 192 ◽

pp. 658-665

Author(s):

Guy Caseneuve ◽

Iren Valova ◽

Nathan LeBlanc ◽

Melanie Thibodeau

Keyword(s):

Disease Classification ◽

Image Preprocessing ◽

X Ray ◽

Chest X Ray

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Study on Public Chest X-ray Data sets for Lung Disease Classification

2021 3rd International Conference on Signal Processing and Communication (ICPSC) ◽

10.1109/icspc51351.2021.9451726 ◽

2021 ◽

Author(s):

Sreena V G ◽

Narain Ponraj ◽

Deepa P L

Keyword(s):

Lung Disease ◽

Disease Classification ◽

Data Sets ◽

X Ray ◽

Chest X Ray

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Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development

Scientific Data ◽

10.1038/s41597-021-00863-5 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Alexandros Karargyris ◽

Satyananda Kashyap ◽

Ismini Lourentzou ◽

Joy T. Wu ◽

Arjun Sharma ◽

...

Keyword(s):

Deep Learning ◽

Eye Tracking ◽

Tracking System ◽

Eye Gaze ◽

Disease Classification ◽

Radiology Report ◽

Human Machine Interaction ◽

Report Generation ◽

X Ray ◽

Chest X Ray

AbstractWe developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

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MS-ANet: deep learning for automated multi-label thoracic disease detection and classification

PeerJ Computer Science ◽

10.7717/peerj-cs.541 ◽

2021 ◽

Vol 7 ◽

pp. e541

Author(s):

Jing Xu ◽

Hui Li ◽

Xiu Li

Keyword(s):

Image Classification ◽

Response Characteristic ◽

Disease Classification ◽

Practical Significance ◽

X Ray ◽

Multi Scale ◽

Main Challenge ◽

Chest Disease ◽

Chest X Ray ◽

Different Response

The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful information from data. A novel loss function is also designed to improve the rationality of visual perception and multi-label image classification, which forces the consistency of attention regions before and after image transformation. A comprehensive experiment was carried out on the Chest X-Ray14 and CheXpert datasets, separately containing over 100,000 frontal-view and 200,000 front and side view X-ray images with 14 diseases. The AUROC is 0.850 and 0.815 respectively on the two data sets, which achieve the state-of-the-art results, verified the effectiveness of this method in chest X-ray image classification. This study has important practical significance for using AI algorithms to assist radiologists in improving work efficiency and diagnostic accuracy.

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