TensorLayer

Dong, Hao; Supratak, Akara; Luo, Ming Ronnier; Liu, Fangde; Oehmichen, Axel; Yu, Simiao; Guo, Yike

doi:10.1145/3123266.3129391

Cited by 59 publications

(8 citation statements)

References 16 publications

(14 reference statements)

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“…The algorithms were implemented on Python 3.4.3. We used Tensorflow 22 GPU version 1.3.0 and some functions of Tensorlayer 23 library version 1.6.4. The CUDA version was 8.0.61.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip

et al. 2020

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Purpose Sonographic diagnosis of developmental dysplasia of the hip allows treatment with a flexion-abduction orthosis preventing hip luxation. Accurate determination of alpha and beta angles according to Graf is crucial for correct diagnosis. It is unclear if algorithms could predict the angles. We aimed to compare the accuracy for users and automation reporting root mean squared errors (RMSE). Materials and Methods We used 303 306 ultrasound images of newborn hips collected between 2009 and 2016 in screening consultations. Trained physicians labelled every second image with alpha and beta angles during the consultations. A random subset of images was labeled with time and precision under lab conditions as ground truth. Automation predicted the two angles using a convolutional neural network (CNN). The analysis was focused on the alpha angle. Results Three methods were implemented, each with a different abstraction of the problem: (1) CNNs that directly learn the angles without any post-processing steps; (2) CNNs that return the relevant landmarks in the image to identify the angles; (3) CNNs that return the base line, bony roof line, and the cartilage roof line which are necessary to calculate the angles. The RMSE between physicians and ground truth were found to be 7.1° for alpha. The best CNN architecture was (2) landmark detection. The RMSE between landmark detection and ground truth was 3.9° for alpha. Conclusion The accuracy of physicians in their daily routine is inferior to deep learning-based algorithms for determining angles in ultrasound of the newborn hip. Similar methods could be used to support physicians.

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“…The algorithms were implemented on Python 3.4.3. We used Tensorflow 22 GPU version 1.3.0 and some functions of Tensorlayer 23 library version 1.6.4. The CUDA version was 8.0.61.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip

et al. 2020

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“…The networks were implemented using TensorLayer (Dong et al , 2017) and Tensorflow (Abadi et al , 2016). The detailed description of the networks used in this paper is described in Supplementary Methods.…”

Section: Methods and Datasetsmentioning

confidence: 99%

Evaluation of methods for generative modeling of cell and nuclear shape

Ruan¹,

Murphy

2018

Bioinformatics

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Motivation Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited. Results Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods. Availability and implementation All software and data is available at http://murphylab.cbd.cmu.edu/software. Supplementary information Supplementary data are available at Bioinformatics online.

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“…We then compute the average of these images as the final prediction, with another map showing the P -value of each pixel. We use Tensorflow combined with TensorLayer ( Dong et al , 2017 ) to implement the deep learning module. Trained on a workstation with one Pascal Titan X, the model gets converged in around 8 h.…”

Section: Methodsmentioning

confidence: 99%

DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

Liu

Zhang

et al. 2018

Bioinformatics

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MotivationSuper-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.ResultsHere, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.Availability and implementationThe main program is available at https://github.com/lykaust15/DLBISupplementary information Supplementary data are available at Bioinformatics online.

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TensorLayer

Cited by 59 publications

References 16 publications

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip

Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip

Evaluation of methods for generative modeling of cell and nuclear shape

DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

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