TY - GEN
T1 - Medical image retrieval and automatic annotation
T2 - 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007
AU - Kalpathy-Cramer, Jayashree
AU - Hersh, William
PY - 2008
Y1 - 2008
N2 - Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.
AB - Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.
UR - http://www.scopus.com/inward/record.url?scp=70349788974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349788974&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85760-0_79
DO - 10.1007/978-3-540-85760-0_79
M3 - Conference contribution
AN - SCOPUS:70349788974
SN - 3540857591
SN - 9783540857594
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 623
EP - 630
BT - Advances in Multilingual and Multimodal Information Retrieval - 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Revised Selected Papers
PB - Springer-Verlag
Y2 - 19 September 2007 through 21 September 2007
ER -