Pairwise based deep ranking hashing for histopathology image classification and retrieval

Shi, Xiaoshuang; Sapkota, Manish; Xing, Fuyong; Liu, Fujun; Cui, Lei; Yang, Lin

doi:10.1016/j.patcog.2018.03.015

Cited by 81 publications

(36 citation statements)

References 16 publications

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“…In order to handle the large scale, Shi et al [83] proposed a hashing algorithm that extracts features from images and learns their binary representations. e authors model the pairwise matrix and an objective function with deeplearning framework that learns the binary representations of images.…”

Section: Cbir Research Using Deep-learning Techniquesmentioning

confidence: 99%

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

232

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Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

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Section: Cbir Research Using Deep-learning Techniquesmentioning

confidence: 99%

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

232

View full text Add to dashboard Cite

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“…Much effort for accelerating the PageRank algorithm has been carried out from different view, such as Monte Carlo method [16], random walk [17], power method or general linear system [18,19], graph theory [20, 21,22], Schrödinger equation [23], and quantum networks [24,25]. More recent related advances on this topic can be found in [26,27,28,29,30,31,32,33,34,35,36].…”

Section: Related Workmentioning

confidence: 99%

Two-hop walks indicate PageRank order

Tang

2019

Pattern Recognition

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This paper shows that pairwise PageRank orders emerge from two-hop walks.The main tool used here refers to a specially designed sign-mirror function and a parameter curve, whose low-order derivative information implies pairwise PageRank orders with high probability. We study the pairwise correct rate by placing the Google matrix G in a probabilistic framework, where G may be equipped with different random ensembles for model-generated or realworld networks with sparse, small-world, scale-free features, the proof of which is mixed by mathematical and numerical evidence. We believe that the underlying spectral distribution of aforementioned networks is responsible for the high pairwise correct rate. Moreover, the perspective of this paper naturally leads to an O(1) algorithm for any single pairwise PageRank comparison if assuming both A = G − I n , where I n denotes the identity matrix of order n, and A 2 are ready on hand (e.g., constructed offline in an incremental manner), based on which it is easy to extract the top k list in O(kn), thus making it possible for PageRank algorithm to deal with super large-scale datasets in real time.

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“…Furthermore, the application of CBIR and feature extraction approaches has become increasingly common in medical image analysis. Shi et al 26 proposed a deep‐learning framework that extracts features from images and learns of their binary representations, whereby a classification accuracy of 97.94% was achieved for experiments conducted on thousands of histopathology images. Cai Y et al 27 proposed a new content‐based medical image retrieval (CBMIR) framework via integrating a CNN with hash coding to effectively analyze computed tomography (CT) scans.…”

Section: Introductionmentioning

confidence: 99%

Content‐based gastric image retrieval using convolutional neural networks

Zheng

et al. 2020

Int J Imaging Syst Tech

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The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content-based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet-18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet-18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state-of-art conventional methods. Furthermore, we constructed a Gastric-Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.

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Pairwise based deep ranking hashing for histopathology image classification and retrieval

Cited by 81 publications

References 16 publications

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Two-hop walks indicate PageRank order

Content‐based gastric image retrieval using convolutional neural networks

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