Does deep learning always outperform simple linear regression in optical imaging?

Jiao, Shuming; Gao, Yang; Feng, Jun; Lei, Ting; Yuan, Xiaocong

doi:10.1364/oe.382319

Cited by 56 publications

(22 citation statements)

References 47 publications

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“…As a result, the Cobb angle can be estimated independent of recording conditions such as illumination of a room or human skin color. A DLA trained and tested for a certain category of samples may not work when it is generalized to different test samples [21]. In fact, the accuracy of the DLA by Yang et al [15] was lower in the external data set than in the internal data set and the area under the curve in the receiver operating characteristic decreased from 0.95 to 0.81 when detecting scoliosis with a curve ≥10˚ [15].…”

Section: Discussionmentioning

confidence: 99%

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

et al. 2021

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BACKGROUND CONTEXT: Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. PURPOSE: This study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS. STUDY DESIGN: Retrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. PATIENT SAMPLE: One hundred and-sixty human subjects suspected to have AIS were included. OUTCOME MEASURES: Patient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study. METHODS: One hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no studyspecific biases related to conflicts of interest. RESULTS: The correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0˚and 5.4˚, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10˚and 89% for that of ≥20˚. FDA device/drug status: Not approved (SCOLIOMAP).

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Section: Discussionmentioning

confidence: 99%

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

et al. 2021

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“…Linear Regression: If there is a linear relationship between the predicted data and the variables in the study, linear regression is preferred [37]. Linear Regression curve and the representation of the data are given in Figure 6.…”

Section: Regressionmentioning

confidence: 99%

Investigation of Cloud Computing Based Big Data on Machine Learning Algorithms.

Yıldırım

Çınar

Cengil

2021

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Cloud computing technology is a model that allows access to a common pool of configurable computing resources whenever and wherever. With the developing technology, the use of this model is increasing day by day. There are many benefits of cloud computing to its users. The data that users keep in their data sets is the simplest example of this. With the cloud technology, the size of the data stored in databases is also increasing. For this reason, cloud technology and big data concepts are intertwined due to the large amount of data stored in databases. It is of great importance that the obtained data is evaluated by machine learning methods and produces results that can be used for technical and commercial purposes. In this study, first of all, cloud technology, the big data brought by this technology and the classification of these data with machine learning methods and algorithms have been examined. Then the studies in the literature were evaluated.

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“…We demonstrate that using DL it is possible to correctly classify two neuroblastoma cell lines with an accuracy of 100%. It is important to underline that the comparison among different learning strategies allows to identify a trade-off between the classification accuracy and the computational complexity of the selected learning method [30]. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

Priscoli

Memmolo

Ciaparrone

et al. 2021

IEEE J. Select. Topics Quantum Electron.

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The label-free single cell analysis by machine and Deep Learning, in combination with digital holography in transmission microscope configuration, is becoming a powerful framework exploited for phenotyping biological samples. Usually, quantitative phase images of cells are retrieved from the reconstructed complex diffraction patterns and used as inputs of a deep neural network. However, the phase retrieval process can be very time consuming and prone to errors. Here we address the classification of cells by using learning strategies with images coming directly from the raw recorded digital holograms, i.e. without any data processing or refocusing involved. Indeed, in the raw digital hologram the entire complex amplitude information of the sample is intrinsically embedded in the form of modulated fringes. We develop a training strategy, based on deep and feature based machine learning models, in order extract such information by skipping the classical reconstruction process for classifying different neuroblastoma cells. We provided an experimental validation by using the proposed strategy to classify two neuroblastoma cell lines.

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Does deep learning always outperform simple linear regression in optical imaging?

Cited by 56 publications

References 47 publications

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

Investigation of Cloud Computing Based Big Data on Machine Learning Algorithms.

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

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