A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

Lian, Jie; Liu, Jingyu; Zhang, Shu; Gao, Kai; Liu, Xiaoqing; Zhang, Dingwen; Yu, Yizhou

doi:10.1109/tmi.2021.3070847

Cited by 26 publications

(19 citation statements)

References 47 publications

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“…AP25 and AP75 are used as references. We believe that AP75 better reflects the accurate positioning performance of the fracture region due to its strict evaluation criteria, AP25 has relatively loose evaluation criteria to determine whether the test results are misjudged and thus better reflects the recognition performance of rib abnormalities, AP50 is a comprehensive performance index for fracture recognition and regional positioning [42,43].…”

Section: Evaluation Metricsmentioning

confidence: 99%

CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules

Gao

Liu

Jiang

et al. 2022

Biomedical Signal Processing and Control

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Section: Evaluation Metricsmentioning

confidence: 99%

CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules

Gao

Liu

Jiang

et al. 2022

Biomedical Signal Processing and Control

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“…Due to the excellent feature extraction abilities of convolutional neural networks (CNNs), deep learning has been increasingly used in the field of medical disease diagnosis in recent years [14][15][16], in fields such as dermatology [17][18][19][20], ophthalmology [21,22], and radiology [23,24]. Shen et al applied fluorescence imaging and deep convolutional neural networks to diagnose intraoperative gliomas in real time for the rapid and accurate identification of gliomas during surgery [25].…”

Section: Related Workmentioning

confidence: 99%

A cell phone app for facial acne severity assessment

et al. 2022

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Acne vulgaris, the most common skin disease, can cause substantial economic and psychological impacts to the people it affects, and its accurate grading plays a crucial role in the treatment of patients. In this paper, we firstly proposed an acne grading criterion that considers lesion classifications and a metric for producing accurate severity ratings. Due to similar appearance of acne lesions with comparable severities and difficult-to-count lesions, severity assessment is a challenging task. We cropped facial skin images of several lesion patches and then addressed the acne lesion with a lightweight acne regular network (Acne-RegNet). Acne-RegNet was built by using a median filter and histogram equalization to improve image quality, a channel attention mechanism to boost the representational power of network, a region-based focal loss to handle classification imbalances and a model pruning and feature-based knowledge distillation to reduce model size. After the application of Acne-RegNet, the severity score is calculated, and the acne grading is further optimized by the metadata of the patients. The entire acne assessment procedure was deployed to a mobile device, and a phone app was designed. Compared with state-of-the-art lightweight models, the proposed Acne-RegNet significantly improves the accuracy of lesion classifications. The acne app demonstrated promising results in severity assessments (accuracy: 94.56%) and showed a dermatologist-level diagnosis on the internal clinical dataset.The proposed acne app could be a useful adjunct to assess acne severity in clinical practice and it enables anyone with a smartphone to immediately assess acne, anywhere and anytime.

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“…Numerous studies have verified that CNNs for lung disease identification can produce diagnosis results with accuracy meeting that of radiologists, such as ChestX-ray14, which is used public datasets of National Institutes of Health (24), and the CheXNeXt, which is based on the DenseNet (25). However, research for critical cases or cases in older adults were not mentioned in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea

Liu

Chiu²,

Huang³

et al. 2022

Front. Med.

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Pneumonia and pulmonary edema are the most common causes of acute respiratory failure in emergency and intensive care. Airway maintenance and heart function preservation are two foundations for resuscitation. Laboratory examinations have been utilized for clinicians to early differentiate pneumonia and pulmonary edema; however, none can provide results as prompt as radiology examinations, such as portable chest X-ray (CXR), which can quickly deliver results without mobilizing patients. However, similar features between pneumonia and pulmonary edema are found in CXR. It remains challenging for Emergency Department (ED) physicians to make immediate decisions as radiologists cannot be on-site all the time and provide support. Thus, Accurate interpretation of images remains challenging in the emergency setting. References have shown that deep convolutional neural networks (CNN) have a high sensitivity in CXR readings. In this retrospective study, we collected the CXR images of patients over 65 hospitalized with pneumonia or pulmonary edema diagnosis between 2016 and 2020. After using the ICD-10 codes to select qualified patient records and removing the duplicated ones, we used keywords to label the image reports found in the electronic medical record (EMR) system. After that, we categorized their CXR images into five categories: positive correlation, negative correlation, no correlation, low correlation, and high correlation. Subcategorization was also performed to better differentiate characteristics. We applied six experiments includes the crop interference and non-interference categories by GoogLeNet and applied three times of validations. In our best model, the F1 scores for pneumonia and pulmonary edema are 0.835 and 0.829, respectively; accuracy rate: 83.2%, Recall rate: 83.2%, positive predictive value: 83.3%, and F1 Score: 0.832. After the validation, the best accuracy rate of our model can reach up to 73%. The model has a high negative predictive value of excluding pulmonary edema, meaning the CXR shows no sign of pulmonary edema. At the time, there was a high positive predictive value in pneumonia. In that way, we could use it as a clinical decision support (CDS) system to rule out pulmonary edema and rule in pneumonia contributing to the critical care of the elderly.

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A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

Cited by 26 publications

References 47 publications

CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules

CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules

A cell phone app for facial acne severity assessment

Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea

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