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A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Pérez, Andrés D.; Perdomo, Oscar; Rios, Hernán; Rodríguez, Francisco; González, Fabio A. (Springer VerlagAlemania, 2020)

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper ...
How do our students learn clinical engineering? A pilot study

Cruz, Antonio Miguel; Quiroga Torres, Daniel Alejandro; Presiga, Ana Maria; Flórez Luna, Nestor (Springer Verlag, 2017)

This paper aims to measure what are the students’ perceived learning outcome achievements after finishing their clinical engineering major courses. This is a pre- post-test with no control group study design. Forty ...
Teaching maintenance of medical devices in simulation centers: a pilot study

Quiroga Torres, Daniel Alejandro; Presiga, Ana Maria; Flórez Luna, Nestor; Cruz, Antonio Miguel (Springer VerlagAlemania, 2016)

This paper aims to measure what the students’ perceived learning outcome achievements are after finishing medical devices courses I and II. This is a pre- post-test with no control group study design. Twenty-four students ...

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A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
...
Pérez, Andrés D. | 2020

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.

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How do our students learn clinical engineering? A pilot study
...
Cruz, Antonio Miguel | 2017

This paper aims to measure what are the students’ perceived learning outcome achievements after finishing their clinical engineering major courses. This is a pre- post-test with no control group study design. Forty students were involved in this pilot study. A paper-based survey composed of a demographic section and a 5-point Likert (“1” is strongly disagree and “5” is strongly agree) section measured the students’ perceived learning outcome achievements after exposing them to clinical engineering major courses. A Wilcoxon signed-rank and Mann-Whitney U test statistics were conducted to test the two hypotheses of this study. Our analysis showed statistically significant results between the pre-survey mean and SD: 21.10 SD 3.54; and between the postsurvey mean and SD: 22.75 SD 3.68 (Z -2.12, p<0.033), indicating that overall, students’ perceived learning outcome achievements after exposing them to clinical engineering major courses had significantly improved by the end of the major. Also, statistically significant results were found between the post-survey mean and SD: 3.94 SD 0.61 learning outcome perceptions and between the students’ actual marks mean and SD: 4.53 SD 0.22 (-5.00, p<0.000), indicating the students had low confidence in their learning outcomes after completing their clinical engineering major.

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Teaching maintenance of medical devices in simulation centers: a pilot study
...
Quiroga Torres, Daniel Alejandro | 2016

This paper aims to measure what the students’ perceived learning outcome achievements are after finishing medical devices courses I and II. This is a pre- post-test with no control group study design. Twenty-four students doing medical devices courses I and II with a test in the simulation center of a Biomedical Engineering Program participated in this pilot study. A paper-based survey composed of a demographic and 5-point Likert (“1” is strongly disagree and “5” is strongly agree) measured the students’ perceived learning outcome achievements after exposing them to medical devices courses. A Wilcoxon signed-rank test and Mann-Whitney U Test statistics were conducted to test the two hypotheses of this study. Our analysis showed statistically significant results between the pre-survey sum mean and SD: 7.50, 1.31; and between the post-survey mean and SD: 8.56, 1.15, p=0.015, indicating the students’ perceived learning outcome achievements after putting them through medical devices courses I and II had significantly improved by the end of the courses. Also, no statistically significant results were found between the post-survey mean and SD: 4.37, 0.57 learning outcome perceptions or between the students’ actual marks mean and SD: 4.58, 0.18, p=0.28), indicating no differences between what the students perceived they had learned compared with what they had really learned.

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Repositorio Digital

Tipo de Material

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

How do our students learn clinical engineering? A pilot study

Teaching maintenance of medical devices in simulation centers: a pilot study

Envíos recientes

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

How do our students learn clinical engineering? A pilot study

Teaching maintenance of medical devices in simulation centers: a pilot study

Tipo de Material

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement ﻿

How do our students learn clinical engineering? A pilot study ﻿

Teaching maintenance of medical devices in simulation centers: a pilot study ﻿

Envíos recientes

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

How do our students learn clinical engineering? A pilot study

Teaching maintenance of medical devices in simulation centers: a pilot study

A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

How do our students learn clinical engineering? A pilot study

Teaching maintenance of medical devices in simulation centers: a pilot study