scholarly journals Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

2018 ◽  
Author(s):  
Juan C. Caicedo ◽  
Jonathan Roth ◽  
Allen Goodman ◽  
Tim Becker ◽  
Kyle W Karhohs ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Source Code ◽  
Evaluation Framework ◽  
Evaluation Methodology ◽  
Biologically Relevant ◽  
Recent Developments ◽  
Classical Image ◽  
Microscopy Images ◽  
Nucleus Segmentation

Identifying nuclei is often a critical first step in analyzing microscopy images of cells, and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half.

scholarly journals Supervised Promoter Recognition: A benchmark framework

2021 ◽  
Author(s):  
Raul Ivan Perez Martell ◽  
Alison Ziesel ◽  
Hosna Jabbari ◽  
Ulrike Stege
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Source Code ◽  
Evaluation Process ◽  
Regulatory Sequences ◽  
Biologically Relevant ◽  
Performance Improvements ◽  
Promoter Recognition ◽  
Benchmark Datasets ◽  
Complete Process

AbstractDeep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models have continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are being evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each model’s true performance difficult to assess.We present a framework called Supervised Promoter Recognition Framework (‘SUPR REF’) capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models in a systematic manner. SUPR REF includes the creation of biologically relevant benchmark datasets to be used in the evaluation process of deep learning promoter recognition models. We showcase this framework by comparing the models’ performance on alternative datasets, and properly evaluate previously published models on new benchmark datasets. Our results show that the reliability of deep learning ab initio promoter recognition models on eukaryotic genomic sequences is still not at a sufficient level, as precision is severely lacking. Furthermore, given the observational nature of these data, cross-validation results from small datasets need to be interpreted with caution.AvailabilitySource code and documentation of the framework is available online at https://github.com/ivanpmartell/suprref

scholarly journals YeastMate: Neural network-assisted segmentation of mating and budding events in S. cerevisiae

2021 ◽  
Author(s):  
David Bunk ◽  
Julian Moriasy ◽  
Felix Thoma ◽  
Christopher Jakubke ◽  
Christof Osman ◽  
...  
Keyword(s):  
Neural Network ◽  
Saccharomyces Cerevisiae ◽  
Deep Learning ◽  
Source Code ◽  
Automated Detection ◽  
Daughter Cells ◽  
Mother And Daughter ◽  
User Friendly ◽  
Microscopy Images

Here, we introduce YeastMate, a user-friendly deep learning- based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

scholarly journals Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

2021 ◽  
Vol 11 (19) ◽  
pp. 8802
Author(s):  
Ilias Papadeas ◽  
Lazaros Tsochatzidis ◽  
Angelos Amanatiadis ◽  
Ioannis Pratikakis
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Real Time ◽  
Current Trend ◽  
Autonomous Driving ◽  
Evaluation Framework ◽  
Future Research ◽  
Comprehensive Overview ◽  
Semantic Image Segmentation ◽  
Recent Developments

Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to operate in real time so that can efficiently support an autonomous driving scenario. To this end, the presented overview puts a particular emphasis on the presentation of all those approaches which permit inference time reduction, while an analysis of the existing methods is addressed by taking into account their end-to-end functionality, as well as a comparative study that relies upon a consistent evaluation framework. Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep learning for autonomous driving.

scholarly journals A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6928
Author(s):  
Maximilian Schmoeller ◽  
Christian Stadter ◽  
Michael Karl Kick ◽  
Christian Geiger ◽  
Michael Friedrich Zaeh
Keyword(s):  
Quality Assurance ◽  
Deep Learning ◽  
Process Monitoring ◽  
Test Methods ◽  
Evaluation Framework ◽  
Evaluation Methodology ◽  
Internal Defects ◽  
Subsequent Work ◽  
Weld Seams

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.

scholarly journals A Novel Approach to the Holistic 3D Characterization of Weld Seams – Paving the Way for Deep Learning-Based Process Monitoring

2021 ◽  
Author(s):  
Maximilian Schmoeller ◽  
Christian Stadter ◽  
Michael Karl Kick ◽  
Christian Geiger ◽  
Michael Friedrich Zaeh
Keyword(s):  
Quality Assurance ◽  
Deep Learning ◽  
Process Monitoring ◽  
Test Methods ◽  
Evaluation Framework ◽  
Evaluation Methodology ◽  
Internal Defects ◽  
Novel Approach ◽  
Weld Seams

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing.

scholarly journals Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

2019 ◽  
Vol 95 (9) ◽  
pp. 952-965 ◽  
Author(s):  
Juan C. Caicedo ◽  
Jonathan Roth ◽  
Allen Goodman ◽  
Tim Becker ◽  
Kyle W. Karhohs ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Fluorescence Images ◽  
Nucleus Segmentation

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Literature survey of deep learning-based vulnerability analysis on source code

IET Software ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 654-664
Author(s):  
Abubakar Omari Abdallah Semasaba ◽  
Wei Zheng ◽  
Xiaoxue Wu ◽  
Samuel Akwasi Agyemang
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Vulnerability Analysis ◽  
Literature Survey

Deep learning strategies for critical heat flux detection in pool boiling

2021 ◽  
Vol 190 ◽  
pp. 116849
Author(s):  
Seyed Moein Rassoulinejad-Mousavi ◽  
Firas Al-Hindawi ◽  
Tejaswi Soori ◽  
Arif Rokoni ◽  
Hyunsoo Yoon ◽  
...  
Keyword(s):  
Heat Flux ◽  
Deep Learning ◽  
Learning Strategies ◽  
Critical Heat Flux ◽  
Pool Boiling
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