Abstract
A computer-aided detection (CADe) system identifies features on a medical image and brings them to the radiologist’s attention. However, the spread of CADe systems in clinical sites is limited. Our aim is to establish a generalized framework for developing various CADe systems. The framework would provide opportunities for many clinicians without the CADe system expertise to develop and use such systems that meet their specific needs. We proposed a pilot version of the framework, including four pretrained algorithms: preprocessing, extraction of a candidate area, candidate detection, and candidate classification. We experimentally confirmed that two different types of CADe system were developed successfully through the generalized framework using two different datasets. We also proposed two feature generation methods to improve the generalized framework. One is the use of multiple deep convolutional autoencoders (DCAEs) trained with a normal dataset. The other is by transfer using a deep convolutional neural network (DCNN) pretrained with an anatomical landmark dataset from whole-body CT. An evaluation of these methods using head MR angiography datasets shows that the DCAEs could extract useful features for lesion classification.
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Nemoto, M., Hayashi, N. (2022). AI: A Machine-Learning-Based Framework for Developing Various Computer-Aided Detection Systems with Generated Image Features. In: Hashizume, M. (eds) Multidisciplinary Computational Anatomy. Springer, Singapore. https://doi.org/10.1007/978-981-16-4325-5_49
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DOI: https://doi.org/10.1007/978-981-16-4325-5_49
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