Compact Root Bilinear CNNs for Content-Based Image Retrieval

Alzu’bi, Ahmad; Amira, Abbes; Ramzan, Naeem

doi:10.1109/icivc.2016.7571271

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…At present, most methods of natural image analysis based on network can be divided into strong supervision, semi-supervision, or weakly supervision. [18][19][20][21][22] Wei et al 20 first applied the unsupervised object location method for fine-grained image retrieval in 2017.…”

Section: Fine-grained Image Retrievalmentioning

confidence: 99%

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Centralized contrastive loss with weakly supervised progressive feature extraction for fine‐grained common thorax disease retrieval in chest x‐ray

et al. 2023

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Background Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image‐based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x‐ray images. Although extensive efforts have been put into this field, there are still many challenges. Purpose Most of the existing fine‐grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high‐level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine‐grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine‐grained medical images still has the problem of insufficient sample data with accurate annotation information. Methods To address above challenges, we introduced a novel fine‐grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x‐ray datasets. Results Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26%∼4.6%$\%{\sim }4.6\%$ and achieves a very efficient training speed which is 100 times faster than the pair‐wise loss‐based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrieval performance. Conclusions The proposed model is capable of learning discriminative representations from chest x‐ray datasets, and it achieves better performance compared with other state‐of‐the‐art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chest disease.

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Section: Fine-grained Image Retrievalmentioning

confidence: 99%

“…Fine‐grained image retrieval 17 fuses various existing fine‐grained image datasets with traditional image retrieval by constructing a hierarchical database. At present, most methods of natural image analysis based on network can be divided into strong supervision, semi‐supervision, or weakly supervision 18–22 . Wei et al 20 .…”

Section: Related Workmentioning

confidence: 99%

Centralized contrastive loss with weakly supervised progressive feature extraction for fine‐grained common thorax disease retrieval in chest x‐ray

et al. 2023

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“…Machin Learning is an intelligent methodology that has emerged as promising technique in the domains of classification and prediction [34,35]. In SDN, the flow rule setup causes delays in controllers' response time.…”

Section: Machine Learning Based Optimizationmentioning

confidence: 99%

A Review of the Control Plane Scalability Approaches in Software Defined Networking

Abuarqoub¹

2020

Future Internet

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Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.

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“…Another advanced feature of CNNs is representation learning, in which the weights are associated with the optimal and automatic feature learning detectors. Over the past five years of applications, CNNs have been demonstrated to exhibit unrivaled performance [25]. Due to their great achievement, CNNs are commonly applied to medical image processing [26].…”

Section: ) Step I: Deep Cnnsmentioning

confidence: 99%

Automatic Segmentation of the Left Ventricle From Cardiac MRI Using Deep Learning and Double Snake Model

Lan

Jin²

2019

IEEE Access

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The left ventricle segmentation (LVS) is of great important for the evaluation of cardiac function. This study aimed to establish new segmentation algorithms that can enhance the accuracy and robustness of automatic LVS on magnetic resonance images. The datasets involved 45 subjects, including 12 heart failure patients with ischemia, 12 heart failure patients without ischemia, 12 hypertrophy patients and 9 normal individuals. The experiments consisted of three important steps. At first, deep learning was employed for the coarse LVS on myocardial images. Next, a double snake model was applied to assess the endo-and epi-cardial boundaries. Finally, the optimal epicardial boundary was obtained by adopting radial region growing method. Additionally, the performance of the developed LVS method was evaluated by the previously established software. Furthermore, the developed LVS method was validated by applying the datasets of 45 subjects. The results showed that the good contours, overlapping dice metric and average perpendicular distance of both epi-and endo-cardial contours were approximately 97%, 0.97 and 1.8 mm respectively. The regression coefficient and coefficient of determination between the proposed method and clinical experts were 0.96 and 1.039, respectively for ejection fraction, while 0.92 and 0.994 for left ventricle mass. These findings reveal that the developed method can enhance the accuracy and robustness of LVS. This novel LVS approach exhibits outstanding performance and possesses promising potential to increase the reliability of computer-aided imaging detection system for cardiovascular disease.INDEX TERMS Convolutional neural network, snake model, left ventricle segmentation, magnetic resonance imaging, cardiac functional analysis.

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Compact Root Bilinear CNNs for Content-Based Image Retrieval

Cited by 13 publications

References 10 publications

Centralized contrastive loss with weakly supervised progressive feature extraction for fine‐grained common thorax disease retrieval in chest x‐ray

Centralized contrastive loss with weakly supervised progressive feature extraction for fine‐grained common thorax disease retrieval in chest x‐ray

A Review of the Control Plane Scalability Approaches in Software Defined Networking

Automatic Segmentation of the Left Ventricle From Cardiac MRI Using Deep Learning and Double Snake Model

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