A Dual-Tree Complex Wavelet Transform Based Convolutional Neural Network for Human Thyroid Medical Image Segmentation

Lu, Hongya; Wang, Haifeng; Zhang, Qianqian; Won, Daehan; Yoon, Sang Won

doi:10.1109/ichi.2018.00029

Cited by 32 publications

(23 citation statements)

References 13 publications

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“…Convolutional neural networks (CNN) are applied on ophthalmology 2D OCT images for the segmentation and assessment of the retinal layers [45][46][47] and the identification of retinal pathological conditions [48,49], whereas Abdolmanafi, et al used deep learning to identify coronal artery layers in OCT images [50]. Recently, 2D thyroid OCT images were used to test the performance of a CNN for medical image segmentation that was able to classify follicular, non-follicular and background regions [51]. Different CNN architectures were proposed for improving the segmentation performance by increasing the depth of the network and adjusting the number of convolutional and fully connected layers [52].…”

Section: Deep Learningmentioning

confidence: 99%

Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure

Tampu¹,

Maintz²,

Koller³

et al. 2020

Biomed. Opt. Express

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To investigate the potential of optical coherence tomography (OCT) to distinguish between normal and pathologic thyroid tissue, 3D OCT images were acquired on ex vivo thyroid samples from adult subjects (n=22) diagnosed with a variety of pathologies. The follicular structure was analyzed in terms of count, size, density and sphericity. Results showed that OCT images highly agreed with the corresponding histopatology and the calculated parameters were representative of the follicular structure variation. The analysis of OCT volumes provides quantitative information that could make automatic classification possible. Thus, OCT can be beneficial for intraoperative surgical guidance or in the pathology assessment routine.

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Section: Deep Learningmentioning

confidence: 99%

Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure

Tampu¹,

Maintz²,

Koller³

et al. 2020

Biomed. Opt. Express

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show abstract

“…Haar wavelet responses of the input image have been concatenated to features at different stages of CNN to address texture classification [19]. Lu et al [20] designed a similar approach for medical image segmentation, however based on dual-tree complex wavelets. Robustness to scale and orientation of CNN is increased by modulating learned filters by a set of Gabor filters [21].…”

Section: B Wavelets and Cnnsmentioning

confidence: 99%

Harmonic Networks with Limited Training Samples

Uličný

Krylov

Dahyot

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. However this typically requires abundant labelled training data to estimate the filter parameters. Alternative strategies have been deployed for reducing the number of parameters and / or filters to be learned and thus decrease overfitting. In the context of reverting to preset filters, we propose here a computationally efficient harmonic block that uses Discrete Cosine Transform (DCT) filters in CNNs. In this work we examine the performance of harmonic networks in limited training data scenario. We validate experimentally that its performance compares well against scattering networks that use wavelets as preset filters.

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“…In that study, DT-CWT was used in a one-dimensional signal separation of gear audio signals. In 2018, Lu et al [51] proposed an algorithm using these two methods for human thyroid medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Yaşar

Ceylan

2021

Cogn Comput

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Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral groundglass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.

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A Dual-Tree Complex Wavelet Transform Based Convolutional Neural Network for Human Thyroid Medical Image Segmentation

Cited by 32 publications

References 13 publications

Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure

Optical coherence tomography for thyroid pathology: 3D analysis of tissue microstructure

Harmonic Networks with Limited Training Samples

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

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