Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Sketching for knowledge capture: a progress report
Proceedings of the 7th international conference on Intelligent user interfaces
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Intelligent Systems
Assisted browsing in a diagnostic image database
AVI '96 Proceedings of the workshop on Advanced visual interfaces
A collaborative intelligent tutoring system for medical problem-based learning
Proceedings of the 9th international conference on Intelligent user interfaces
Neural Computation
Resolving ambiguities to create a natural computer-based sketching environment
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Clinical-reasoning skill acquisition through intelligent group tutoring
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Artificial Intelligence in Medicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Guest editorial: Artificial Intelligence in Medicine AIME '05
Artificial Intelligence in Medicine
An agent-based framework for sketched symbol interpretation
Journal of Visual Languages and Computing
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Objective: Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Methods: Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician. Results: The sketch recognition algorithm achieves a recognition accuracy of 75.5%, far above the baseline random classification accuracy of 6.7%. Comparison of the results of the part identification algorithm with the judgment of an experienced physician shows close agreement in terms of location, orientation, size, and shape of the identified parts. Conclusions: The performance of our prototype in terms of accuracy and running time provides strong evidence that development of robust sketch understanding systems for medical domains is an attainable goal. Further work needs to be done to extend the approach to sketches containing multiple and partial anatomical structures, as well as to be able to interpret sketch annotations.