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Adversarial image reconstruction learning framework for medical image retrieval

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Abstract

Due to the advancement in digital recording techniques, a large amount of data (images/video) is created in medical centers. Penetrating this indistinguishable data is a challenging task for many healthcare applications. Handling massive data with hand-crafted feature-based techniques is a difficult and time-consuming task. To solve this problem, a robust adversarial image reconstruction learning framework is proposed for medical image retrieval. The proposed approach consists of two stages viz feature extraction through adversarial image reconstruction and index matching followed by retrieval module to retrieve the similar images to that of input medical image. Initially, the adversarial image reconstruction network (AIR-Net) is proposed to encode the input medical image into set of features followed by the reconstruction of the input medical image from the encoded features. These encoded features give latent representation for robust reconstruction for the input image. Therefore, these encoded features are used in the index matching and retrieval module for medical image retrieval task. We use the self-attention mechanism in the proposed AIR-Net to suppress feature redundancy and enhance feature learning ability. The performance of the proposed framework is analyzed on benchmark medical image databases such as OASIS, ILD, VIA/ELCAP-CT for image retrieval task. To examine the robustness of the proposed framework over the existing state-of-the-art approaches, the retrieval accuracy in terms of average precision and recall is compared. From the experimental analysis, it is observed that the proposed approach outperforms the other existing approaches for medical image retrieval task

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  1. JNTUA, Anatapuramu, India

    Rohini Pinapatruni & Shoba Bindu Chigarapalle

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  1. Rohini Pinapatruni
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  2. Shoba Bindu Chigarapalle
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Pinapatruni, R., Chigarapalle, S.B. Adversarial image reconstruction learning framework for medical image retrieval. SIViP 16, 1197–1204 (2022). https://doi.org/10.1007/s11760-021-02070-6

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  • DOI: https://doi.org/10.1007/s11760-021-02070-6

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