Abstract
Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24.
Highlights
In the era of big data, how to help doctors complete pathological diagnosis has become a hot topic for researchers
(3) In order to further ensure the safety of histopathological retrieval, a new privacy protection algorithm is proposed based on 5-D hyperchaotic system and DNA coding technology
In order to further improve the accuracy and safety of pathological search in computer-aided diagnosis, this paper first studies the asymmetric residual hash algorithm to improve the accuracy of histopathological image retrieval based on ResNet-50 model and asymmetric pairwise loss
Summary
In the era of big data, how to help doctors complete pathological diagnosis has become a hot topic for researchers. We first present a novel ARH algorithm for histopathological search to improve the performance of CAD, and we use the 5-D hyperchaotic system to protect histopathological image privacy. Our research goal is to improve the accuracy of histopathological retrieval while protecting patient privacy. The research work has practical application value and has the pioneering method in histopathological retrieval It is manifested in the following two aspects: (1) In histopathological image retrieval, we first engage in related research and optimize the loss function of the algorithm based on the residual hash method. (2) The ciphertext domain image retrieval method was first applied to histopathological image retrieval, and we improved the retrieval scheme and privacy protection algorithm. The structure of this paper is organized as follows: The related works are introduced in section II; The proposed method is presented in section III; The implementation results and analysis are described in section IV; The summary of this paper is presented in V
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