Sketch-based Medical Image Retrieval
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Takaaki Mizuno, Mototaka, Miyake, Hirokazu Watanabe, Masamichi Takahashi, Yasuyuki Takamizawa, Yukihiro, Yoshida, Satoshi Nakamura, Nobuji Kouno, Amina Bolatkan, Yusuke Kurose,, Tatsuya Harada, Ryuji Hamamoto

TL;DR
This paper presents a novel sketch-based medical image retrieval system that allows users to find images without example images by decomposing image features into normal and abnormal components, enabling more flexible and fine-grained retrieval.
Contribution
The paper introduces a new sketch-based retrieval system utilizing feature decomposition into normal and abnormal features, facilitating image retrieval without example images and for isolated samples.
Findings
Enabled retrieval without example images
Improved fine-grained image retrieval
Validated with user tests involving healthcare professionals
Abstract
The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors; nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIR system provides an easy-to-use two-step…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications
MethodsTest
