Deep Convolutional Neural Network with Segmentation Techniques for Chest X-Ray Analysis

Wang, Binquan; Wu, Zeyuan; Khan, Zakir; Liu, Cheng‐Lin; Zhu, Ming

doi:10.1109/iciea.2019.8834117

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…A few recent works in the medical field include applying machine learning to classify necrotic sections of pressure wounds ( Zahia et al, 2018 ), segment brain scans ( Lai et al, 2019 ), and segment chest X-rays ( Wang et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Computer vision, the study of using computers to extract information from images and videos, has become embedded in new, broad medical applications due to the high accuracy that deep learning models can achieve. Recent deep learning models have shown to be effective at solving a variety of vision tasks in medical image analysis like analyzing chest X-rays ( Irvin et al, 2019 ; Wang et al, 2019 ), segmenting brain scans ( Lai et al, 2019 ), and annotating pressure wounds ( Zahia et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Learning Medical Materials From Radiography Images

Molder

Lowe

Zhan

2021

Front. Artif. Intell.

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Deep learning models have been shown to be effective for material analysis, a subfield of computer vision, on natural images. In medicine, deep learning systems have been shown to more accurately analyze radiography images than algorithmic approaches and even experts. However, one major roadblock to applying deep learning-based material analysis on radiography images is a lack of material annotations accompanying image sets. To solve this, we first introduce an automated procedure to augment annotated radiography images into a set of material samples. Next, using a novel Siamese neural network that compares material sample pairs, called D-CNN, we demonstrate how to learn a perceptual distance metric between material categories. This system replicates the actions of human annotators by discovering attributes that encode traits that distinguish materials in radiography images. Finally, we update and apply MAC-CNN, a material recognition neural network, to demonstrate this system on a dataset of knee X-rays and brain MRIs with tumors. Experiments show that this system has strong predictive power on these radiography images, achieving 92.8% accuracy at predicting the material present in a local region of an image. Our system also draws interesting parallels between human perception of natural materials and materials in radiography images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Medical Materials From Radiography Images

Molder

Lowe

Zhan

2021

Front. Artif. Intell.

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“…The current instance segmentation network mainly adds a Mask branch to the object detection network, such as Mask R-CNN [ 12 ], Blend Mask [ 13 ], and Hybrid Task Cascade [ 14 ]. Instance segmentation is applied to segment lung fields, heart, clavicles, and ribs in chest radiographs [ 38 , 39 ]; pelvis [ 40 ]; delineate spinal midline [ 41 ]; and to identify unknown bodies by tooth [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

Accurate Instance Segmentation in Pediatric Elbow Radiographs

Wei

Wang

et al. 2021

Sensors

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Radiography is an essential basis for the diagnosis of fractures. For the pediatric elbow joint diagnosis, the doctor needs to diagnose abnormalities based on the location and shape of each bone, which is a great challenge for AI algorithms when interpreting radiographs. Bone instance segmentation is an effective upstream task for automatic radiograph interpretation. Pediatric elbow bone instance segmentation is a process by which each bone is extracted separately from radiography. However, the arbitrary directions and the overlapping of bones pose issues for bone instance segmentation. In this paper, we design a detection-segmentation pipeline to tackle these problems by using rotational bounding boxes to detect bones and proposing a robust segmentation method. The proposed pipeline mainly contains three parts: (i) We use Faster R-CNN-style architecture to detect and locate bones. (ii) We adopt the Oriented Bounding Box (OBB) to improve the localizing accuracy. (iii) We design the Global-Local Fusion Segmentation Network to combine the global and local contexts of the overlapped bones. To verify the effectiveness of our proposal, we conduct experiments on our self-constructed dataset that contains 1274 well-annotated pediatric elbow radiographs. The qualitative and quantitative results indicate that the network significantly improves the performance of bone extraction. Our methodology has good potential for applying deep learning in the radiography’s bone instance segmentation.

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“…Wang et al [32] surveyed deep learning for image superresolution. They targeted anomaly detection in different areas, aiming to review firstly: research deep-learning-based methods for anomaly detection, and secondly their applications in various domains along with corresponding assessments.…”

Section: Surveys On Deep Learning In Medical Images Analysismentioning

confidence: 99%

“…Another segmentation system was proposed by Wang et al [32], which is applied to chest X-rays, basically using a CNN to extract the feature map of the image along with the classification result, regression result, and the needed mask.…”

Section: Segmentationmentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Deep Convolutional Neural Network with Segmentation Techniques for Chest X-Ray Analysis

Cited by 7 publications

References 9 publications

Learning Medical Materials From Radiography Images

Learning Medical Materials From Radiography Images

Accurate Instance Segmentation in Pediatric Elbow Radiographs

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

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