3D Reconstruction Based on Style Transfer Data Augmentation

2020 ◽  
Vol 140 (11) ◽  
pp. 1198-1206
Author(s):  
Taiki Saruwatari ◽  
Katsufumi Inoue ◽  
Michifumi Yoshioka
Keyword(s):  
3D Reconstruction ◽  
Data Augmentation ◽  
Style Transfer ◽  
Transfer Data

scholarly journals StyPath: Style-Transfer Data Augmentation for Robust Histology Image Classification

2020 ◽  
pp. 351-361 ◽  
Author(s):  
Pietro Antonio Cicalese ◽  
Aryan Mobiny ◽  
Pengyu Yuan ◽  
Jan Becker ◽  
Chandra Mohan ◽  
...  
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Style Transfer ◽  
Transfer Data

scholarly journals Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2605 ◽  
Author(s):  
Rafael Anicet Zanini ◽  
Esther Luna Colombini
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Augmentation ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Treatment Approaches ◽  
Adversarial Networks

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient’s tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.

scholarly journals Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

2021 ◽  
Vol 11 (9) ◽  
pp. 842
Author(s):  
Shruti Atul Mali ◽  
Abdalla Ibrahim ◽  
Henry C. Woodruff ◽  
Vincent Andrearczyk ◽  
Henning Müller ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Data Augmentation ◽  
Image Data ◽  
Clinical Decision ◽  
Generative Adversarial Networks ◽  
Imaging Protocol ◽  
Image Domain ◽  
Style Transfer ◽  
Augmentation Techniques

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.

MO078DEEP LEARNING DIAGNOSIS OF ANTIBODY-MEDIATED REJECTION (AMR) ON GLOMERULAR TRANSECTIONS

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Pietro A Cicalese ◽  
Syed A Rizvi ◽  
Candice Roufosse ◽  
Ibrahim Batal ◽  
Martin Hellmich ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Interobserver Variability ◽  
Training Set ◽  
Style Transfer ◽  
Antibody Mediated Rejection ◽  
Learning Classifier ◽  
Common Causes ◽  
Level Performance ◽  
Fold Cross Validation

Abstract Background and Aims Antibody-mediated rejection (AMR) is among the most common causes for kidney transplant loss. The histological diagnosis is hampered by significant intra- and interobserver variability. Training a deep learning classifier for the recognition of AMR on glomerular transections as the most decisive compartment could establish a reliable and perfectly reproducible diagnostic method. Method We identified 48 biopsies with AMR (all positive for donor-specific antibody) and 38 biopsies without AMR according to Banff 2017 from our archive. Photographs were taken from all non-globally sclerosed glomeruli on two PAS-stained level sections, yielding a total of 1,655 images as a training set. 1,503 images could be labeled by three experienced nephropathologists conclusively as AMR or non-AMR in a blinded fashion. We trained a DenseNet-121 classifier (pre-trained on ImageNet) with basic online augmentation. In addition, we implemented StyPath++, a data augmentation algorithm that leverages a style transfer mechanism, addressing significant domain shifts in histopathology. Each sample was assigned a consensus label generated by the pathologists. Results Five-fold cross validation schemes produced a weighted glomerular level performance of 88.1%, exceeding the baseline performance by 5%. The improved generalization ability of the StyPath++ augmented model shows that it is possible to construct reliable glomerular classification algorithms with scarce datasets. Conclusion We created a deep learning classifier with excellent performance and reproducibility for the diagnosis of AMR on glomerular transections. We plan to expand the training set, including challenging cases of differential diagnoses like glomerulonephritis or other glomerulopathies. We are also interested in external clinicopathological datasets to further validate our results.

scholarly journals Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet

2020 ◽  
Vol 13 (6) ◽  
pp. 349-363
Author(s):  
I Darma ◽  
◽  
Nanik Suciati ◽  
Daniel Siahaan ◽  
◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Training Dataset ◽  
Geometric Transformation ◽  
Style Transfer ◽  
Geometric Transformations ◽  
Feature Extractor ◽  
Improve Accuracy

The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.

scholarly journals 3D city scale reconstruction using wide area motion imagery

2018 ◽  
Author(s):  
◽  
Raphael Viguier
Keyword(s):  
3D Reconstruction ◽  
Data Compression ◽  
Data Augmentation ◽  
Point Clouds ◽  
Wide Area ◽  
Large Field ◽  
Reconstruction Algorithms ◽  
Detection And Tracking ◽  
3D Point Clouds ◽  
Motion Imagery

3D reconstruction is one of the most challenging but also most necessary part of computer vision. It is generally applied everywhere, from remote sensing to medical imaging and multimedia. Wide Area Motion Imagery is a field that has gained traction over the recent years. It consists in using an airborne large field of view sensor to cover a typically over a square kilometer area for each captured image. This is particularly valuable data for analysis but the amount of information is overwhelming for any human analyst. Algorithms to efficiently and automatically extract information are therefore needed and 3D reconstruction plays a critical part in it, along with detection and tracking. This dissertation work presents novel reconstruction algorithms to compute a 3D probabilistic space, a set of experiments to efficiently extract photo realistic 3D point clouds and a range of transformations for possible applications of the generated 3D data to filtering, data compression and mapping. The algorithms have been successfully tested on our own datasets provided by Transparent Sky and this thesis work also proposes methods to evaluate accuracy, completeness and photo-consistency. The generated data has been successfully used to improve detection and tracking performances, and allows data compression and extrapolation by generating synthetic images from new point of view, and data augmentation with the inferred occlusion areas.

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