An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis.
Learning medical image interpretation is an evolutive process that requires modular training systems, from non-expert to expert users. Our study aims at developing such a system for endomicroscopy diagnosis. It uses a difficulty predictor to try and shorten the physician learning curve. As the understanding of video diagnosis is driven by visual similarities, we propose a content-based video retrieval approach to estimate the level of interpretation difficulty. The performance of our retrieval method is compared with several state of the art methods, and its genericity is demonstrated with two different clinical databases, on the Barrett's Esophagus and on colonic polyps. From our retrieval results, we learn a difficulty predictor against a ground truth given by the percentage of false diagnoses among several physicians. Our experiments show that, although our datasets are not large enough to test for statistical significance, there is a noticeable relationship between our retrieval-based difficulty estimation and the difficulty experienced by the physicians.
André
Barbara
B
Mauna Kea Technologies, Paris.
Vercauteren
Tom
T
Buchner
Anna M
AM
Shahid
Muhammad Waseem
MW
Wallace
Michael B
MB
Ayache
Nicholas
N
D001185:Artificial Intelligence; D053704:Capsule Endoscopy / Q000379:methods*; D003194:Computer-Assisted Instruction / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface