Conference proceeding
Weakly supervised classification of medical images
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.110-113
05/2012
DOI: 10.1109/ISBI.2012.6235496
Abstract
A weakly supervised image classification framework is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, we learn to automatically detect relevant patterns, i.e. patterns that only appear in relevant images. After training, relevant patterns are sought in unseen images in order to classify each image as relevant or irrelevant. No manual segmentations are required. Because manual segmentation of medical images is extremely time-consuming, existing classification algorithms are usually trained on limited reference datasets. With the proposed framework, much larger medical datasets are now available for training. The proposed approach has been successfully applied to diabetic retinopathy detection in a retinal image dataset (A z =0.855).
Details
- Title: Subtitle
- Weakly supervised classification of medical images
- Creators
- G Quellec - Inserm, Brest, FranceM Laniard - Univ. Bretagne Occidentale, Brest, FranceG Cazuguel - Dept. ITI, UEB, Brest, FranceM. D Abramoff - Dept. of Ophthalmology & Visual Sci., Univ. of Iowa, Iowa City, IA, USAB Cochener - Univ. Bretagne Occidentale, Brest, FranceC Roux - Dept. ITI, UEB, Brest, France
- Resource Type
- Conference proceeding
- Publication Details
- 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.110-113
- Publisher
- IEEE
- DOI
- 10.1109/ISBI.2012.6235496
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 05/2012
- Academic Unit
- Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Ophthalmology and Visual Sciences; Fraternal Order of Eagles Diabetes Research Center
- Record Identifier
- 9984060619602771
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