scholarly journals Unsupervised content-preserving transformation for optical microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

scholarly journals Unsupervised content-preserving transformation for optical microscopy

2019 ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

The development of deep learning and the open access to a substantial collection of imaging data provide a potential solution to computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner and the reliance on a laborious and error-prone data annotation procedure remains a barrier towards more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases where supervised models cannot apply. By introducing a saliency constraint, the unsupervised model, dubbed as Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data and avoid the distortion of the image content. UTOM shows promising performances in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations elucidate that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

scholarly journals Interactive biomedical segmentation tool powered by deep learning and ImJoy

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 142
Author(s):  
Wei Ouyang ◽  
Trang Le ◽  
Hao Xu ◽  
Emma Lundberg
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Training Data ◽  
Fine Tuning ◽  
Interactive Machine Learning ◽  
Biomedical Image ◽  
Wide Range ◽  
Human Protein Atlas ◽  
Bioimage Analysis ◽  
User Friendly

Deep learning-based methods play an increasingly important role in bioimage analysis. User-friendly tools are crucial for increasing the adoption of deep learning models and efforts have been made to support them in existing image analysis platforms. Due to hardware and software complexities, many of them have been struggling to support re-training and fine-tuning of models which is essential  to avoid  overfitting and hallucination issues  when working with limited training data. Meanwhile, interactive machine learning provides an efficient way to train models on limited training data. It works by gradually adding new annotations by correcting the model predictions while the model is training in the background. In this work, we developed an ImJoy plugin for interactive training and an annotation tool for image segmentation. With a small example dataset obtained from the Human Protein Atlas, we demonstrate that CellPose-based segmentation models can be trained interactively from scratch within 10-40 minutes, which is at least 6x faster than the conventional annotation workflow and less labor intensive. We envision that the developed tool can make deep learning segmentation methods incrementally adoptable for new users and be used in a wide range of applications for biomedical image segmentation.

scholarly journals Deep learning classifiers for near infrared spectral imaging: a tutorial

2020 ◽  
Author(s):  
Jun-Li Xu ◽  
Cecilia Riccioli ◽  
Ana Herrero-Langreo ◽  
Aoife Gowen
Keyword(s):  
Deep Learning ◽  
Visual Recognition ◽  
Near Infrared ◽  
Three Dimensional ◽  
Spectral Imaging ◽  
Learning Approaches ◽  
Imaging Data ◽  
Spectral Image ◽  
Background Removal ◽  
Wide Range

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

scholarly journals On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dennis Segebarth ◽  
Matthias Griebel ◽  
Nikolai Stein ◽  
Cora R von Collenberg ◽  
Corinna Martin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Biological Effects ◽  
Reliability And Validity ◽  
Ground Truth ◽  
Training Data ◽  
Model Organisms ◽  
Data Annotation ◽  
Bioimage Analysis ◽  
Model Training

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

scholarly journals Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

2020 ◽  
Vol 12 (9) ◽  
pp. 1379 ◽  
Author(s):  
Yi-Ting Cheng ◽  
Ankit Patel ◽  
Chenglu Wen ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Point Clouds ◽  
Concrete Pavement ◽  
Training Data ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Lane Marking ◽  
Wide Range ◽  
Lane Width

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.

scholarly journals Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training

2020 ◽  
Author(s):  
Caroline Bivik Stadler ◽  
Martin Lindvall ◽  
Claes Lundström ◽  
Anna Bodén ◽  
Karin Lindman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Data ◽  
Ground Truth ◽  
Lessons Learned ◽  
Training Data ◽  
Imaging Data ◽  
High Quality ◽  
High Quality Image ◽  
Specific Objective ◽  
Wide Range

Abstract Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.

scholarly journals Hybrid Deep Learning Models for Sentiment Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Cach N. Dang ◽  
María N. Moreno-García ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Computation Time ◽  
Hybrid Models ◽  
Training Data ◽  
Support Vector ◽  
Learning Models ◽  
Hybrid Techniques ◽  
Wide Range

Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

scholarly journals Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7378
Author(s):  
Pedro M. R. Bento ◽  
Jose A. N. Pombo ◽  
Maria R. A. Calado ◽  
Silvio J. P. S. Mariano
Keyword(s):  
Deep Learning ◽  
New England ◽  
Network Architecture ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Constant Field ◽  
Short Term ◽  
Wide Range ◽  
Short Term Load Forecasting

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

scholarly journals 2D-to-3D image translation of complex nanoporous volumes using generative networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Timothy I. Anderson ◽  
Bolivia Vega ◽  
Jesse McKinzie ◽  
Saman A. Aryana ◽  
Anthony R. Kovscek
Keyword(s):  
Deep Learning ◽  
Reactive Transport ◽  
Ion Beam ◽  
Training Data ◽  
Imaging Modalities ◽  
High Contrast ◽  
Imaging Data ◽  
Contrast Imaging ◽  
Apparent Permeability ◽  
Image Translation

AbstractImage-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

scholarly journals Deep learning-based methods for individual recognition in small birds

2019 ◽  
Author(s):  
André C. Ferreira ◽  
Liliana R. Silva ◽  
Francesco Renna ◽  
Hanja B. Brandl ◽  
Julien P. Renoult ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Individual Recognition ◽  
Bird Species ◽  
Training Data ◽  
Individual Identification ◽  
List Type ◽  
Major Advance ◽  
General Applicability ◽  
Automated Method

ABSTRACTIndividual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well established but often make data collection and analyses time consuming and consequently are not suited for collecting very large datasets.Recent technological and analytical advances, such as deep learning, can help overcome these limitations by automatizing data collection and analysis. Currently one of the bottlenecks preventing the application of deep learning for individual identification is the need of hundreds to thousands of labelled pictures required for training convolutional neural networks (CNNs).Here, we describe procedures that improve data collection and allow individual identification in captive and wild birds and we apply it to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata.First, we present an automated method that allows the collection of large samples of individually labelled images. Second, we describe how to train a CNN to identify individuals. Third, we illustrate the general applicability of CNN for individual identification in animal studies by showing that the trained CNN can predict the identity of birds from images collected in contexts that differ from the ones originally used to train the CNNs. Fourth, we present a potential solution to solve the issues of new incoming individuals.Overall our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the lab and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual identification of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

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