Computational Analysis and Deep Learning for Medical Care

2021 ◽  
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Computational Analysis

scholarly journals Quality control stress test for deep learning-based diagnostic model in digital pathology

2021 ◽  
Author(s):  
Birgid Schömig-Markiefka ◽  
Alexey Pryalukhin ◽  
Wolfgang Hulla ◽  
Andrey Bychkov ◽  
Junya Fukuoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Stress Testing ◽  
Computational Analysis ◽  
Stress Test ◽  
Digital Pathology ◽  
Model Performance ◽  
Diagnostic Model ◽  
Model Accuracy ◽  
Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

scholarly journals Technology-Powered Strategies to Rethink the Pedagogy of History and Cultural Heritage through Symmetries and Narratives

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 367 ◽  
Author(s):  
Martín López-Nores ◽  
Omar Bravo-Quezada ◽  
Maddalena Bassani ◽  
Angeliki Antoniou ◽  
Ioanna Lykourentzou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Digital Humanities ◽  
Teaching And Learning ◽  
Computational Analysis ◽  
Learning Technologies ◽  
Cross Border ◽  
New Information ◽  
New Strategies

Recent advances in semantic web and deep learning technologies enable new means for the computational analysis of vast amounts of information from the field of digital humanities. We discuss how some of the techniques can be used to identify historical and cultural symmetries between different characters, locations, events or venues, and how these can be harnessed to develop new strategies to promote intercultural and cross-border aspects that support the teaching and learning of history and heritage. The strategies have been put to the test in the context of the European project CrossCult, revealing enormous potential to encourage curiosity to discover new information and increase retention of learned information.

scholarly journals Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

2020 ◽  
Author(s):  
Alex Dexter ◽  
Spencer A. Thomas ◽  
Rory T. Steven ◽  
Kenneth N. Robinson ◽  
Adam J. Taylor ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Computational Analysis ◽  
New Technologies ◽  
State Of The Art ◽  
Current State ◽  
Data Representations ◽  
Non Linear ◽  
Linear Dimensionality Reduction

AbstractHigh dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

scholarly journals Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

2017 ◽  
Author(s):  
Chuangqi Wang ◽  
Shawn Kang ◽  
Eunice Kim ◽  
Xitong Zhang ◽  
Hee June Choi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Cell Migration ◽  
Deep Learning ◽  
Computational Analysis ◽  
Time Lapse ◽  
Collective Cell Migration ◽  
Edge Detector ◽  
Cell Protrusion ◽  
Canny Edge

AbstractCell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. In this paper we combined Canny edge detector, Deep Neural Networks (DNNs), and local intensity thresholding. We were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells using both simple convolutional neural networks and VGG-16 based neural networks. We used relatively small effort to prepare the training set to train the DNNs to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 10013-10014
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED’s objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospitalspecific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED’s performance metrics.

scholarly journals A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

2020 ◽  
Vol 10 (1) ◽  
pp. 421 ◽  
Author(s):  
Kwang Sun Ryu ◽  
Sang Won Lee ◽  
Erdenebileg Batbaatar ◽  
Jae Wook Lee ◽  
Kui Son Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Smoking Status ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Undiagnosed Diabetes ◽  
Training Dataset ◽  
Family History Of Diabetes ◽  
Screening Model ◽  
Deep Learning Model

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 695-701 ◽  
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.

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