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Deep learning in biomedical engineering

Deep Learning in medicine is one of the most rapidly and new developing fields of science. Currently, almost every device intended for medical imaging has a more or less extended image and signal analysis and processing module which can use deep learning. It provides quantitative data necessary to make a diagnosis. The obtained quantitative features must be independent of the inter-subject variability and the type of medical device and, above all, must allow for reproducible results in the presence of high noise. The proposed deep learning algorithms should also ensure the independence of the results obtained by the operator of the imaging device and, to be more exact, its position relative to the patient or the parameter settings in the device. In addition, the proposed deep learning algorithms must be tailored for the diagnosis of a specific disease entity. On the other hand, they must allow for reproducible results for high inter-subject variability. These criteria make it difficult to propose a methodology for the deep learning algorithms. This special issue of BioMedical Engineering Online is dedicated to this area of knowledge.

Topics:

  • Deep neural network in medical image processing (RTG, USG, CT, PET, OCT and others)
  • New deep neural network architecture
  • The use of applications with deep machine learning for recognizing objects in a 3D scene
  • Deep machine learning in large data sets
  • Deep robot learning
  • Data mining with deep learning in bioinformatics
  • Applications, algorithms and tools directly related to deep learning

Edited by Robert Koprowski


  1. The Corvis® ST tonometer is an innovative device which, by combining a classic non-contact tonometer with an ultra-fast Scheimpflug camera, provides a number of parameters allowing for the assessment of corneal b...

    Authors: Magdalena Jędzierowska, Robert Koprowski, Sławomir Wilczyński and Katarzyna Krysik

    Citation: BioMedical Engineering OnLine 2019 18:115

    Content type: Research

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  2. Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been deemed as a potential method for UCAI due to its’ high frame rate and ...

    Authors: Meng Dai, Shuying Li, Yuanyuan Wang, Qi Zhang and Jinhua Yu

    Citation: BioMedical Engineering OnLine 2019 18:95

    Content type: Research

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  3. Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus image...

    Authors: Noushin Eftekhari, Hamid-Reza Pourreza, Mojtaba Masoudi, Kamaledin Ghiasi-Shirazi and Ehsan Saeedi

    Citation: BioMedical Engineering OnLine 2019 18:67

    Content type: Research

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  4. Ankle joint function in a paretic limb has a fundamental impact on mobility. Return of joint function is a measure of early poststroke physical rehabilitation. This study aims to assess the suitability of usin...

    Authors: Ewa Chlebuś, Agnieszka Wareńczak, Margaret Miedzyblocki and Przemysław Lisiński

    Citation: BioMedical Engineering OnLine 2019 18:57

    Content type: Research

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  5. Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm...

    Authors: Agnese Sbrollini, Marjolein C. De Jongh, C. Cato Ter Haar, Roderick W. Treskes, Sumche Man, Laura Burattini and Cees A. Swenne

    Citation: BioMedical Engineering OnLine 2019 18:15

    Content type: Research

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  6. Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage gr...

    Authors: Yunzhi Huang, Luyi Han, Haoran Dou, Honghao Luo, Zhen Yuan, Qi Liu, Jiang Zhang and Guangfu Yin

    Citation: BioMedical Engineering OnLine 2019 18:8

    Content type: Research

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