Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

2018 ◽  
pp. 130-138 ◽  
Author(s):  
John-Melle Bokhorst ◽  
Lucia Rijstenberg ◽  
Danny Goudkade ◽  
Iris Nagtegaal ◽  
Jeroen van der Laak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Colorectal Carcinoma ◽  
Automatic Detection ◽  
Tumor Budding

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Automatic detection of papilledema through fundus retinal images using deep learning

2021 ◽  
Author(s):  
Tanzila Saba ◽  
Shahzad Akbar ◽  
Hoshang Kolivand ◽  
Saeed Ali Bahaj
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Retinal Images

scholarly journals Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

2021 ◽  
Vol 273 ◽  
pp. 116490
Author(s):  
Odei Garcia-Garin ◽  
Toni Monleón-Getino ◽  
Pere López-Brosa ◽  
Asunción Borrell ◽  
Alex Aguilar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Automatic Detection ◽  
Aerial Images ◽  
Learning Approach ◽  
Detection And Quantification

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning

2021 ◽  
Vol 266 ◽  
pp. 192-200
Author(s):  
Stephen P. Canton ◽  
Esmaeel Dadashzadeh ◽  
Linwah Yip ◽  
Raquel Forsythe ◽  
Robert Handzel
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Radiology Reports

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Automatic detection of seafloor marine litter using towed camera images and deep learning

2021 ◽  
Vol 164 ◽  
pp. 111974
Author(s):  
Dimitris V. Politikos ◽  
Elias Fakiris ◽  
Athanasios Davvetas ◽  
Iraklis A. Klampanos ◽  
George Papatheodorou
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Marine Litter

Correlation between tumor budding, microvascular density, and β-catenin expression in colorectal carcinoma

2016 ◽  
Vol 36 (1) ◽  
pp. 108-114
Author(s):  
Samar A. El Sheikh ◽  
Amira M. Bassam ◽  
Heba A. Ibrahim
Keyword(s):  
Colorectal Carcinoma ◽  
Microvascular Density ◽  
Tumor Budding
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