The VISual Concept Extraction challenge in RAdioLogy (VISCERAL) project has been developed as a cloud–based infrastructure for the evaluation of medical image data in large data sets. As part of this project, the ISBI 2014 (Inter-national Symposium for Biomedical Imaging) challenge was organized using the VISCERAL data set and shared cloud– framework. Two tasks were selected to exploit and com-pare multiple state–of–the–art solutions designed for big data medical image analysis. Segmentation and landmark localiza-tion results from the submitted algorithms were compared to manually annotated ground truth in the VISCERAL data set. This paper presents an overview of the challenge setup and data set used as well as the evaluation metrics from t...
The medical domain has been an inspiring application area in visualization research for many years a...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...
The University of Essex participated in the fourth edition of the ImageCLEFcaption task which aims t...
Since during clinical routine, only a small portion of increasing amounts of medical imaging data ar...
This is an overview paper describing the data and evaluation scheme of the VISCERAL Segmentation Cha...
VISCERAL (Visual Concept Extraction Challenge in Radiology) aims to organize series of benchmarks on...
Since during clinical routine, only a small portion of the increasing amounts of medical imaging dat...
Variations in the shape and appearance of anatomical structures in medical images are often relevant...
Variations in the shape and appearance of anatomical structures in medical images are often relevant...
Systematic evaluation has had a strong impact on many data analysis domains, for example, TREC and C...
This book presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (...
In the past decades the number of medical images inspected daily in health centers, as well as the c...
This dataset consists of additional images for 100 images from the ISIC 2017 challenge training data...
The 2014 workshop on medical computer vision (MCV): algorithms for big data took place in Cambridge,...
This paper presents an overview of the Medical Visual Question Answering task (VQA-Med) at ImageCLEF...
The medical domain has been an inspiring application area in visualization research for many years a...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...
The University of Essex participated in the fourth edition of the ImageCLEFcaption task which aims t...
Since during clinical routine, only a small portion of increasing amounts of medical imaging data ar...
This is an overview paper describing the data and evaluation scheme of the VISCERAL Segmentation Cha...
VISCERAL (Visual Concept Extraction Challenge in Radiology) aims to organize series of benchmarks on...
Since during clinical routine, only a small portion of the increasing amounts of medical imaging dat...
Variations in the shape and appearance of anatomical structures in medical images are often relevant...
Variations in the shape and appearance of anatomical structures in medical images are often relevant...
Systematic evaluation has had a strong impact on many data analysis domains, for example, TREC and C...
This book presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (...
In the past decades the number of medical images inspected daily in health centers, as well as the c...
This dataset consists of additional images for 100 images from the ISIC 2017 challenge training data...
The 2014 workshop on medical computer vision (MCV): algorithms for big data took place in Cambridge,...
This paper presents an overview of the Medical Visual Question Answering task (VQA-Med) at ImageCLEF...
The medical domain has been an inspiring application area in visualization research for many years a...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...
The University of Essex participated in the fourth edition of the ImageCLEFcaption task which aims t...