A Dataset and Evaluation Framework for Deep Learning Based Video Stabilization Systems

2019 ◽  
Author(s):  
Maria Silvia Ito ◽  
Ebroul Izquierdo
Keyword(s):  
Deep Learning ◽  
Evaluation Framework ◽  
Video Stabilization

scholarly journals Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework & Open Datasets

2021 ◽  
pp. 1-1
Author(s):  
Dongwoon Hyun ◽  
Alycen Wiacek ◽  
Sobhan Goudarzi ◽  
Sven Rothlubbers ◽  
Amir Asif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ultrasound Image ◽  
Image Formation ◽  
Evaluation Framework ◽  
Open Datasets

scholarly journals A Large-Scale Evaluation Framework for EEG Deep Learning Architectures

2018 ◽  
Author(s):  
Felix A. Heilmeyer ◽  
Robin T. Schirrmeister ◽  
Lukas D. J. Fiederer ◽  
Martin Volker ◽  
Joos Behncke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Evaluation Framework ◽  
Scale Evaluation ◽  
Learning Architectures

scholarly journals Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4143
Author(s):  
Michael Barz ◽  
Daniel Sonntag
Keyword(s):  
Deep Learning ◽  
Visual Attention ◽  
Eye Tracking ◽  
Performance Metrics ◽  
Evaluation Framework ◽  
Detection Methods ◽  
Manual Annotation ◽  
Learning Models ◽  
Areas Of Interest ◽  
Mobile Eye Tracking

Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods.

scholarly journals Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox

2021 ◽  
Vol 15 ◽  
Author(s):  
Takuto Okuno ◽  
Alexander Woodward
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Functional Connectivity ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Evaluation Framework ◽  
Series Data ◽  
Ground Truth Data ◽  
Functional Connectome ◽  
Auto Regressive

An important goal in neuroscience is to elucidate the causal relationships between the brain’s different regions. This can help reveal the brain’s functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies. Therefore, we propose a new framework based on a vector auto-regressive deep neural network (VARDNN) architecture. Our approach consists of a set of nodes, each with a deep neural network structure. These nodes can be mapped to any spatial sub-division based on the data to be analyzed, such as anatomical brain regions from which representative neural signals can be obtained. VARDNN learns to reproduce experimental time series data using modern deep learning training techniques. Based on this, we developed two novel directed functional connectivity (dFC) measures, namely VARDNN-DI and VARDNN-GC. We evaluated our measures against a number of existing functional connectome estimation measures, such as partial correlation and multivariate Granger causality combined with large dimensionality counter-measure techniques. Our measures outperformed them across various types of ground truth data, especially as the number of nodes increased. We applied VARDNN to fMRI data to compare the dFC between 41 healthy control vs. 32 Alzheimer’s disease subjects. Our VARDNN-DI measure detected lesioned regions consistent with previous studies and separated the two groups well in a subject-wise evaluation framework. Summarily, the VARDNN framework has powerful capabilities for whole brain dFC estimation. We have implemented VARDNN as an open-source toolbox that can be freely downloaded for researchers who wish to carry out functional connectome analysis on their own data.

scholarly journals A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

2021 ◽  
Author(s):  
Benjamin Haibe-Kains ◽  
Michal Kazmierski ◽  
Mattea Welch ◽  
Sejin Kim ◽  
Chris McIntosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Clinical Data ◽  
Neck Cancer ◽  
Evaluation Framework ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Precision Oncology

Abstract Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

scholarly journals An Adversarial Deep-Learning-Based Model for Cervical Cancer CTV Segmentation With Multicenter Blinded Randomized Controlled Validation

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhikai Liu ◽  
Wanqi Chen ◽  
Hui Guan ◽  
Hongnan Zhen ◽  
Jing Shen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Evaluation System ◽  
Performance Metrics ◽  
Locally Advanced ◽  
Evaluation Framework ◽  
Test Accuracy ◽  
Randomized Controlled ◽  
Ct Data ◽  
Controlled Evaluation

PurposeTo propose a novel deep-learning-based auto-segmentation model for CTV delineation in cervical cancer and to evaluate whether it can perform comparably well to manual delineation by a three-stage multicenter evaluation framework.MethodsAn adversarial deep-learning-based auto-segmentation model was trained and configured for cervical cancer CTV contouring using CT data from 237 patients. Then CT scans of additional 20 consecutive patients with locally advanced cervical cancer were collected to perform a three-stage multicenter randomized controlled evaluation involving nine oncologists from six medical centers. This evaluation system is a combination of objective performance metrics, radiation oncologist assessment, and finally the head-to-head Turing imitation test. Accuracy and effectiveness were evaluated step by step. The intra-observer consistency of each oncologist was also tested.ResultsIn stage-1 evaluation, the mean DSC and the 95HD value of the proposed model were 0.88 and 3.46 mm, respectively. In stage-2, the oncologist grading evaluation showed the majority of AI contours were comparable to the GT contours. The average CTV scores for AI and GT were 2.68 vs. 2.71 in week 0 (P = .206), and 2.62 vs. 2.63 in week 2 (P = .552), with no significant statistical differences. In stage-3, the Turing imitation test showed that the percentage of AI contours, which were judged to be better than GT contours by ≥5 oncologists, was 60.0% in week 0 and 42.5% in week 2. Most oncologists demonstrated good consistency between the 2 weeks (P > 0.05).ConclusionsThe tested AI model was demonstrated to be accurate and comparable to the manual CTV segmentation in cervical cancer patients when assessed by our three-stage evaluation framework.

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 Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment

2021 ◽  
Vol 11 (11) ◽  
pp. 4727
Author(s):  
Hyunjung Kim
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Supply And Demand ◽  
Evaluation Framework ◽  
Automatic Extraction ◽  
Floor Plan ◽  
Deep Learning Algorithm ◽  
Plan Analysis ◽  
Floor Plans

This study proposes a technology that allows automatic extraction of vectorized indoor spatial information from raster images of floor plans. Automatic reconstruction of indoor spaces from floor plans is based on a deep learning algorithm, which trains on scanned floor plan images and extracts critical indoor elements such as room structures, junctions, walls, and openings. The newly developed technology proposed herein can handle complicated floor plans which could not be automatically extracted by previous studies because of its complexity and difficulty in being trained in deep learning. Such complicated reconstruction solely from a floor plan image can be digitized and vectorized either through manual drawing or with the help of newly developed deep learning-based automatic extraction. This study proposes an evaluation framework for assessing this newly developed technology against manual digitization. Using the analytical hierarchy process, the hierarchical aspects of technology value and their relative importance are systematically quantified. The analysis suggested that the automatic technology using a deep learning algorithm had predominant criteria followed by, substitutability, completeness, and supply and demand. In this study, the technology value of automatic floor plan analysis compared with that of traditional manual edits is compared systemically and assessed qualitatively, which had not been done in existing studies. Consequently, this study determines the effectiveness and usefulness of automatic floor plan analysis as a reasonable technology for acquiring indoor spatial information.

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