Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Manual query modification and data fusion for medical image retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Supervised machine learning based medical image annotation and retrieval in ImageCLEFmed 2005
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
The use of MedGIFT and EasyIR for ImageCLEF 2005
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Overview of the ImageCLEFmed 2006 medical retrieval and medical annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Multimodal medical image retrieval OHSU at ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Artificial Intelligence in Medicine
Using media fusion and domain dimensions to improve precision in medical image retrieval
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Biomedical imaging modality classification using combined visual features and textual terms
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
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Oregon Health & Science University participated in both the medical retrieval and medical annotation tasks of ImageCLEF 2006. Our efforts in the retrieval task focused on manual modification of query statements and fusion of results from textual and visual retrieval techniques. Our results showed that manual modification of queries does improve retrieval performance, while data fusion of textual and visual techniques improves precision but lowers recall. However, since image retrieval may be a precision-oriented task, these data fusion techniques could be of value for many users. In the annotation task, we assessed a variety of learning techniques and obtained classification accuracy of up to 74% with test data.