Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation

Zhao, Yu; Lipková, Jana; Wan, Shaohua; Sekuboyina, Anjany; Hu, Xiaobin; Tetteh, Giles; Piraud, Marie; Menze, Bjoern

doi:10.1109/jbhi.2019.2891526

Cited by 173 publications

(107 citation statements)

References 43 publications

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“…After that, GTIC will map each cluster to a specific category. Generally, deep learning is also considered to used in ground truth inferencing in several works [23][24][25][26][27][28]. Albarqouni et al [23] provides an CNN-like network called AggNet to model the crowdworkers and inference process.…”

Section: Related Workmentioning

confidence: 99%

Learning deep networks with crowdsourcing for relevance evaluation

Yin²,

et al. 2020

J Wireless Com Network

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In this paper, we propose a novel relevance evaluation method using labels collected from crowdsourcing. The proposed method not only predicts the relevance between query texts and responses in information retrieval systems but also performs the label aggregation tasks simultaneously. It first merges two kinds of heterogeneous data (i.e., image and query text) and constructs a CNN-like deep neural network. Then, on the top of its softmax layer, an additional layer was built to model the crowd workers. Finally, classification models for relevance prediction and aggregated labels for training examples can be simultaneously learned from noisy labels. Experimental results show that the proposed method significantly outperforms other state-of-the-art methods on a real-world dataset.

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Section: Related Workmentioning

confidence: 99%

Learning deep networks with crowdsourcing for relevance evaluation

Yin²,

et al. 2020

J Wireless Com Network

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show abstract

“…Wan et al [12]- [16] A fast multi-class data retrieval method Wan et al [17]- [20] A data query method based on Internet of Things architecture Zhu et al [21]- [25] An Influence Model based on Heterogeneous Online Social network for Influence Maximization Xiong et al [26] A new method for sharing files Tang et al [27] A query method based on multiple attributes Zhao et al [28]- [34] A privacy protection scheme for multisource data in the cloud. Qi et al [35], [36] A data-driven service recommendation with privacy-preservation.…”

Section: Researchers Contributionmentioning

confidence: 99%

Secure Data De-Duplication Based on Threshold Blind Signature and Bloom Filter in Internet of Things

Huang

et al. 2020

IEEE Access

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Within the cloud environment, the availability of storage, as well as bandwidth, can be effectively preserved in virtue of data de-duplication. However, refraining redundancy from additional storage or communication is not trivial due to security concerns. Though intensive researches have been addressed on a convergent cryptosystem for secure data de-duplication, the conflicts amongst functionality, confidentiality, and authority remain unbalanced. More concretely, although data are obfuscated under convergent encryption, a violent dictionary attack is still efficacious since the whole pseudorandom process relies heavily on plaintexts. As for data ownership, the download privilege, which depends on hash value, may also be infringed due to the same reason. To dispose of these problems, we presented a conspiracy-free data de-duplication protocol based on a threshold blind signature in this paper. With the help of multiple key servers, the outsourced file and de-duplication label will be computationally indistinguishable from random strings. We used the Boom filter as a tool to implement a proof of ownership, ensuring that the ownership claims made by users are real. It effectively prevents the attacker from using the stolen tag to get the whole file to gain file access without authorization. The most significant innovation of this paper is to use homomorphism computation to aggregate and generate partial signature tags, and to introduce a secret sharing mechanism based on The Chinese Remainder Theorem to hide signature keys, thus balancing the security concerns of cloud and client. Compared with existing schemes, both communication and computation performances are preferable in our protocol. As far as we know, our scheme is the only data de-duplication scheme that satisfies the semantic security of ciphertext and label. INDEX TERMS cloud, secure data de-duplication, threshold blind signature, Bloom filter

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“…SVM has been used in many fields, such as pattern recognition, regression, and equalization [35][36][37]. Based on SVM, Qian et al provided a driver identification method with data of steering, brake, and acceleration pedals [38]. Some studies carried on the field test and proposed a driving model to identify the driving intention of the driver by using the SVM [39,40].…”

Section: Introductionmentioning

confidence: 99%

A driver’s car-following behavior prediction model based on multi-sensors data

Wang

et al. 2020

J Wireless Com Network

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The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

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Knowledge-Aided Convolutional Neural Network for Small Organ Segmentation

Cited by 173 publications

References 43 publications

Learning deep networks with crowdsourcing for relevance evaluation

Learning deep networks with crowdsourcing for relevance evaluation

Secure Data De-Duplication Based on Threshold Blind Signature and Bloom Filter in Internet of Things

A driver’s car-following behavior prediction model based on multi-sensors data

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