SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network

Li, Meng; Hsu, William; Xie, Xiaodong; Cong, Jason; Gao, Wen

doi:10.1109/tmi.2020.2968472

Cited by 204 publications

(129 citation statements)

References 51 publications

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“…A self-attention CNN for low-dose CT denoising with self-supervised perceptual loss network. 32 7. A cycle-consistent adversarial network (CycleGAN) for low-dose CT image denoising without paired CT images for training.…”

Section: Performance Comparison Of Cnn-based Image Denois-mentioning

confidence: 99%

“…This belief is supported by the successful use of the data in numerous publications. [26][27][28][29][30][31][32][33][34] The dataset is provided in a paired fashion at both routine dose and simulated lower dose, which is ideal for many techniques involving supervised learning. Many noise reduction techniques trained based on these paired datasets have demonstrated great success in terms of reducing image noise and improving image quality.…”

mentioning

confidence: 99%

“…Many noise reduction techniques trained based on these paired datasets have demonstrated great success in terms of reducing image noise and improving image quality. [26][27][28][29][30][31][32][33][34] However, given the recent success of unsupervised low-dose CT reconstruction, it is desirable also to have unpaired low-dose and full-dose dataset available, which is a topic of interest for future development. Meanwhile, the current paired dataset provided in this work can still be used to meet that purpose using appropriate arrangement of training cases (e.g., organize the cases in an unpaired fashion), which is beneficial as the full-dose images can be used as references even if they are not used in the training and testing.…”

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confidence: 99%

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Low‐dose CT image and projection dataset

et al. 2020

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To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and Validation Methods: The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. Data Format and Usage Notes: Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (

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Section: Performance Comparison Of Cnn-based Image Denois-mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Low‐dose CT image and projection dataset

et al. 2020

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“…Then, we will introduce the DGATNE model in three parts, aggregation [39], concatenation [40] and selfattention mechanism [41]. Aggregation.…”

Section: B the Construction Of Improved Link Prediction Model Dgatnementioning

confidence: 99%

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

Xia

Yin

2021

IEEE Access

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“…Since fusion can improve the performance in many ways [ 23 , 24 , 25 ], Liu et al [ 26 ] proposed a novel network layer that effectively fuses the global information from the input, and a novel multi-scale input strategy that acquires multi-scale features. Li et al [ 27 ] proposed a novel 3D self-attention CNN for the low-dose CT denoising problem; the structure acquired more spatial information. Tschandl et al [ 28 ] trained two CNNs with dermoscopic images and clinical close-ups images, respectively, and combined the outputs of CNNs to diagnose nonpigmented skin cancer.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network

Cited by 204 publications

References 51 publications

Low‐dose CT image and projection dataset

Low‐dose CT image and projection dataset

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

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