scholarly journals VP18.04: Automatic evaluation of the cardiothoracic area ratio and cardiac axis in fetal ultrasound using deep learning

2021 ◽  
Vol 58 (S1) ◽  
pp. 173-173
Author(s):  
K. Shozu ◽  
M. Komatsu ◽  
A. Sakai ◽  
A. Dozen ◽  
R. Komatsu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Ratio ◽  
Automatic Evaluation ◽  
Fetal Ultrasound

An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index

2019 ◽  
Author(s):  
S. Lakshmi ◽  
Deepu Vijayasenan ◽  
David S. Sumam ◽  
Saraswathy Sreeram ◽  
Pooja K Suresh
Keyword(s):  
Deep Learning ◽  
Labeling Index ◽  
Learning Approach ◽  
Automatic Evaluation ◽  
Ki 67

Deep Learning-based Spine Centerline Extraction in Fetal Ultrasound

2021 ◽  
pp. 263-268
Author(s):  
Astrid Franz ◽  
Alexander Schmidt-Richberg ◽  
Eliza Orasanu ◽  
Cristian Lorenz
Keyword(s):  
Deep Learning ◽  
Fetal Ultrasound ◽  
Centerline Extraction

scholarly journals OP01.09: Automatic classification of fetal ultrasound images: a remarkable application of deep learning technology for fetal medicine

2019 ◽  
Vol 54 (S1) ◽  
pp. 86-87
Author(s):  
X.P. Burgos‐Artizuu ◽  
E. Eixarch ◽  
D. Coronado‐Gutierrez ◽  
B. Valenzuela ◽  
E. Bonet‐Carne ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Fetal Medicine

Automatic evaluation of cardiac hypertrophy using cardiothoracic area ratio in chest radiograph images

2012 ◽  
Vol 105 (2) ◽  
pp. 95-108 ◽  
Author(s):  
Muhammad A. Hasan ◽  
Seok-Lyong Lee ◽  
Deok-Hwan Kim ◽  
Myung-Kwan Lim
Keyword(s):  
Cardiac Hypertrophy ◽  
Chest Radiograph ◽  
Area Ratio ◽  
Automatic Evaluation

scholarly journals Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

2021 ◽  
Vol 5 (4) ◽  
pp. 20-25
Author(s):  
S. Yashaswini ◽  
S. S. Shylaja
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cost Function ◽  
Performance Metrics ◽  
Evaluation Criteria ◽  
Ground Truth ◽  
Automatic Evaluation ◽  
Mean Square ◽  
Learning Models ◽  
Classification Tasks

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

Evaluation of English online teaching based on remote supervision algorithms and deep learning

2020 ◽  
pp. 1-12
Author(s):  
Han Yongqing
Keyword(s):  
Deep Learning ◽  
Evaluation System ◽  
Online Teaching ◽  
Student Evaluation ◽  
Teaching Evaluation ◽  
System Structure ◽  
Automatic Evaluation ◽  
Teaching Process ◽  
Remote Supervision ◽  
School Teaching

The English online teaching automatic evaluation system is unstable in actual teaching evaluation. Therefore, how the automatic evaluation system can better adapt to high school teaching also needs a more in-depth theoretical and practical discussion. According to the actual needs of English online teaching, this article combines remote supervision and deep learning algorithms, builds a system structure for the English online teaching evaluation process, and simulates and analyzes the application of supervision algorithms in the teaching process. Moreover, this article evaluates the actions and status of the student’s learning process from the aspects of teacher evaluation and student evaluation, and also scores the teacher’s teaching process. In order to study the practical effect of this system in English online teaching, this paper designs experiments to evaluate the model online English teaching effect. The research results show that the model constructed in this paper has good performance.

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