Smart Image Follow-Up of Black Pigmentation on the Nail With Convolutional Neural Networks

2021 ◽  
Author(s):  
Zhaoying Liu ◽  
Haofan Liu ◽  
Yang Xie ◽  
Yingxue Yao ◽  
Xiaofen Xing ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Black Pigmentation

scholarly journals Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

2019 ◽  
Vol 12 (9) ◽  
pp. 848-852 ◽  
Author(s):  
Renan Sales Barros ◽  
Manon L Tolhuisen ◽  
Anna MM Boers ◽  
Ivo Jansen ◽  
Elena Ponomareva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Convolutional Neural Networks ◽  
Infarct Volume ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Reference Standard ◽  
Computed Tomography Images

Background and purposeInfarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice.ObjectiveTo assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke.Materials and methodsWe included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations.ResultsThe median infarct volume was 48 mL (IQR 15–125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34.ConclusionConvolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach.

Background Rejection using Convolutional Neural Networks

2017 ◽  
Vol 13 (S338) ◽  
pp. 37-39
Author(s):  
Adam Zadrożny ◽  
Beata Goźlińska
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Simulated Data ◽  
Wide Field ◽  
Proof Of Concept ◽  
Field Surveys ◽  
Background Rejection ◽  
Very High

AbstractThe paper presents a proof of concept method of background rejection based on convolutional neural networks (CNN). The method was tested on simulated data and achieved very high accuracy (100%). What is more, method based on CNN is very fast and could be easily applied to wide field surveys. Since early stage results suggest method is very accurate and robust, it could be helpful in creating very low-latency pipelines for EM Follow-up purposes, which will be needed in LIGO-Virgo O3 EM Follow-up.

scholarly journals Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

2020 ◽  
Vol 7 (06) ◽  
Author(s):  
Mariëlle J. A. Jansen ◽  
Hugo J. Kuijf ◽  
Ashis K. Dhara ◽  
Nick A. Weaver ◽  
Geert Jan Biessels ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Patient Specific

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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