scholarly journals Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm

2019 ◽  
Vol 9 (5) ◽  
pp. 439-461 ◽  
Author(s):  
Luca Saba ◽  
Mainak Biswas ◽  
Harman S. Suri ◽  
Klaudija Viskovic ◽  
John R. Laird ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Carotid Stenosis ◽  
Learning Paradigm

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm

2018 ◽  
Vol 155 ◽  
pp. 165-177 ◽  
Author(s):  
Mainak Biswas ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Harman S. Suri ◽  
Luca Saba ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Tissue Characterization ◽  
Learning Paradigm ◽  
Ultrasound Tissue Characterization ◽  
Liver Ultrasound

scholarly journals Expert camouflage-breakers can accurately localize search targets

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Fallon Branch ◽  
Allison JoAnna Lewis ◽  
Isabella Noel Santana ◽  
Jay Hegdé
Keyword(s):  
Deep Learning ◽  
Visual Search ◽  
Target Localization ◽  
Specific Training ◽  
Learning Paradigm ◽  
Accuracy And Precision ◽  
Per Se ◽  
Special Case

AbstractCamouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target ‘pops out’, i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

2017 ◽  
Vol 80 ◽  
pp. 77-96 ◽  
Author(s):  
Tadashi Araki ◽  
Pankaj K. Jain ◽  
Harman S. Suri ◽  
Narendra D. Londhe ◽  
Nobutaka Ikeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Stroke Risk ◽  
Plaque Morphology ◽  
Learning Paradigm ◽  
Carotid Wall

A Deep Learning Paradigm for Automated Face Attendance

2019 ◽  
pp. 39-50
Author(s):  
Rahul Kumar Gupta ◽  
Shreeja Lakhlani ◽  
Zahabiya Khedawala ◽  
Vishal Chudasama ◽  
Kishor P. Upla
Keyword(s):  
Deep Learning ◽  
Learning Paradigm

scholarly journals Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 58683-58699
Author(s):  
Khalil Khan ◽  
Rehan Ullah Khan ◽  
Kashif Ahmad ◽  
Farman Ali ◽  
Kyung-Sup Kwak
Keyword(s):  
Deep Learning ◽  
Learning Paradigm ◽  
Face Segmentation

scholarly journals Identifying Risk Stratification Associated With a Cancer for Overall Survival by Deep Learning-Based CoxPH

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67708-67717 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Fu Ou-Yang ◽  
Li-Yeh Chuang ◽  
Ming-Feng Hou ◽  
...  
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
Risk Stratification

Automated T1 bladder risk stratification based on depth of lamina propria invasion from H and E tissue biopsies: a deep learning approach

2018 ◽  
Author(s):  
Muhammad Khalid Khan Niazi ◽  
Thomas E. Tavolara ◽  
Vidya Arole ◽  
Anil V. Parwani ◽  
Cheryl T. Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Lamina Propria ◽  
Learning Approach

scholarly journals Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0224502 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Hyue Mee Kim ◽  
Min Jeong Kim ◽  
Sungmin Lim ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Deep Learning ◽  
Risk Stratification
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