Publications
2017
1.
Hernandez, Maria Valdes; Gonzalez-Castro, Victor
21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 Artículo de revista
En: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, pp. 1–949, 2017, (Publisher: Springer-Verlag).
Resumen | Enlaces | BibTeX | Etiquetas: 21st, analysis, conference, image, medical, MIUA, understanding
@article{hernandez_21st_2017,
title = {21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017},
author = {Maria Valdes Hernandez and Victor Gonzalez-Castro},
url = {https://www.research.ed.ac.uk/en/publications/21st-annual-conference-on-medical-image-understanding-and-analysi},
year = {2017},
date = {2017-01-01},
journal = {21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017},
pages = {1–949},
abstract = {The proceedings contain 81 papers. The special focus in this conference is on Medical Image Understanding and Analysis. The topics include: End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography; superpixel-based line operator for retinal blood vessel segmentation; automatic detection and identification of retinal vessel junctions in colour fundus photography; fast optic disc segmentation in retinal images using polar transform; a novel technique for splat generation and patch level prediction in diabetic retinopathy; deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images; multi-level trainable segmentation for measuring gestational and yolk sacs from ultrasound images; weakly supervised learning of placental ultrasound images with residual networks; edge aware geometric filter for ultrasound image ENHA; tissues classification of the cardiovascular system using texture descriptors; multidimensional assessments of abdominal aortic aneurysms by magnetic resonance against ultrasound diameter measurements; comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data; evaluating classifiers for atherosclerotic plaque component segmentation in MRI; classification of cross-sections for vascular skeleton extraction using convolutional neural networks; improved CTA coronary segmentation with a volume-specific intensity threshold; multi task fully convolutional network for brain tumour segmentation; classification of cervical-cancer using pap-smear images ...},
note = {Publisher: Springer-Verlag},
keywords = {21st, analysis, conference, image, medical, MIUA, understanding},
pubstate = {published},
tppubtype = {article}
}
The proceedings contain 81 papers. The special focus in this conference is on Medical Image Understanding and Analysis. The topics include: End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography; superpixel-based line operator for retinal blood vessel segmentation; automatic detection and identification of retinal vessel junctions in colour fundus photography; fast optic disc segmentation in retinal images using polar transform; a novel technique for splat generation and patch level prediction in diabetic retinopathy; deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images; multi-level trainable segmentation for measuring gestational and yolk sacs from ultrasound images; weakly supervised learning of placental ultrasound images with residual networks; edge aware geometric filter for ultrasound image ENHA; tissues classification of the cardiovascular system using texture descriptors; multidimensional assessments of abdominal aortic aneurysms by magnetic resonance against ultrasound diameter measurements; comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data; evaluating classifiers for atherosclerotic plaque component segmentation in MRI; classification of cross-sections for vascular skeleton extraction using convolutional neural networks; improved CTA coronary segmentation with a volume-specific intensity threshold; multi task fully convolutional network for brain tumour segmentation; classification of cervical-cancer using pap-smear images ...
2.
Hernandez, Maria Valdes; Castro, Victor Gonzalez
21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 Artículo de revista
En: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, pp. 1–949, 2017, (Publisher: Springer-Verlag).
Resumen | Enlaces | BibTeX | Etiquetas: 21st, analysis, conference, image, medical, MIUA, understanding
@article{valdes_hernandez_21st_2017,
title = {21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017},
author = {Maria Valdes Hernandez and Victor Gonzalez Castro},
url = {https://www.research.ed.ac.uk/en/publications/21st-annual-conference-on-medical-image-understanding-and-analysi},
year = {2017},
date = {2017-01-01},
journal = {21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017},
pages = {1–949},
abstract = {The proceedings contain 81 papers. The special focus in this conference is on Medical Image Understanding and Analysis. The topics include: End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography; superpixel-based line operator for retinal blood vessel segmentation; automatic detection and identification of retinal vessel junctions in colour fundus photography; fast optic disc segmentation in retinal images using polar transform; a novel technique for splat generation and patch level prediction in diabetic retinopathy; deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images; multi-level trainable segmentation for measuring gestational and yolk sacs from ultrasound images; weakly supervised learning of placental ultrasound images with residual networks; edge aware geometric filter for ultrasound image ENHA; tissues classification of the cardiovascular system using texture descriptors; multidimensional assessments of abdominal aortic aneurysms by magnetic resonance against ultrasound diameter measurements; comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data; evaluating classifiers for atherosclerotic plaque component segmentation in MRI; classification of cross-sections for vascular skeleton extraction using convolutional neural networks; improved CTA coronary segmentation with a volume-specific intensity threshold; multi task fully convolutional network for brain tumour segmentation; classification of cervical-cancer using pap-smear images ...},
note = {Publisher: Springer-Verlag},
keywords = {21st, analysis, conference, image, medical, MIUA, understanding},
pubstate = {published},
tppubtype = {article}
}
The proceedings contain 81 papers. The special focus in this conference is on Medical Image Understanding and Analysis. The topics include: End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography; superpixel-based line operator for retinal blood vessel segmentation; automatic detection and identification of retinal vessel junctions in colour fundus photography; fast optic disc segmentation in retinal images using polar transform; a novel technique for splat generation and patch level prediction in diabetic retinopathy; deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images; multi-level trainable segmentation for measuring gestational and yolk sacs from ultrasound images; weakly supervised learning of placental ultrasound images with residual networks; edge aware geometric filter for ultrasound image ENHA; tissues classification of the cardiovascular system using texture descriptors; multidimensional assessments of abdominal aortic aneurysms by magnetic resonance against ultrasound diameter measurements; comparison of automatic vessel segmentation techniques for whole body magnetic resonance angiography with limited ground truth data; evaluating classifiers for atherosclerotic plaque component segmentation in MRI; classification of cross-sections for vascular skeleton extraction using convolutional neural networks; improved CTA coronary segmentation with a volume-specific intensity threshold; multi task fully convolutional network for brain tumour segmentation; classification of cervical-cancer using pap-smear images ...