Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Huang, Zhiwei; Lin, Jie; Xu, Liming; Hui-qian, Wang; Bai, Tong; Pang, Yu; Meen, Teen-­Hang

doi:10.3390/electronics9010190

Cited by 21 publications

(12 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FPN [39] leveraged the prediction of multiscale hierarchy by generating multiple prediction. For CXR image, Huang et al [40] presented weight concatenation method to cooperate global and local feature. Thriving of spatial attention gave inspiration to extract attention from multi-resolution feature map.…”

Section: Multiscale Feature Fusionmentioning

confidence: 99%

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images

Wang

Shuai

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.

show abstract

Section: Multiscale Feature Fusionmentioning

confidence: 99%

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images

Wang

Shuai

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…and the remaining 10 were of similar performance, with model AUROC values ranging from 0.70 to 0.94. Other studies5,[25][26][27] have generally achieved AUROC values that are similarly high.…”

mentioning

confidence: 74%

Artificial Intelligence Solutions for Analysis of X-ray Images

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

show abstract

“…The employment of computer-aided diagnosis systems optimized the performance of the breast cancer diagnosis [9]. Recently, Deep Learning (DL) has played the main role in several medical tasks [10][11][12][13], and the classification and detection of breast cancer [14,15]. The breast cancer classification task is challenging due the complexity of the breast cancer images.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

View full text Add to dashboard Cite

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

show abstract

Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Cited by 21 publications

References 42 publications

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images

Artificial Intelligence Solutions for Analysis of X-ray Images

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Contact Info

Product

Resources

About