Induction of image retrieval knowledge from radiologists' reading instances

Olivia R. Liu Sheng; Namsik Chang; Chih-Ping Wei; Paul Jen-Hwa Hu

doi:10.1117/12.208819

12 May 1995 Induction of image retrieval knowledge from radiologists' reading instances

Olivia R. Liu Sheng, Namsik Chang, Chih-Ping Wei, Paul Jen-Hwa Hu

Proceedings Volume 2435, Medical Imaging 1995: PACS Design and Evaluation: Engineering and Clinical Issues; (1995) https://doi.org/10.1117/12.208819
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

This paper proposes a multi-decision-tree induction (MDTI) approach to image prehanging and discusses how it can facilitate knowledge acquisition and maintenance through the induction of knowledge embedded in radiological image reading cases which have the characteristics of inconsistent retrievals, incomplete input information, and multiple decision outcome classes. We present empirical comparisons of the MDTI approach with backpropagation network algorithm, and the traditional knowledge acquisition approach using the same set of cases in terms of the recall rate, the precision rate, the average number of prior examinations suggested, understandability of the acquired knowledge, and the required learning time. The results show that the MDTI approach outperforms the backpropagation network algorithm in all performance measures studied.

Citation Download Citation

Olivia R. Liu Sheng, Namsik Chang, Chih-Ping Wei, and Paul Jen-Hwa Hu "Induction of image retrieval knowledge from radiologists' reading instances", Proc. SPIE 2435, Medical Imaging 1995: PACS Design and Evaluation: Engineering and Clinical Issues, (12 May 1995); https://doi.org/10.1117/12.208819

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