[Retracted] Deep Unsupervised Hashing for Large‐Scale Cross‐Modal Retrieval Using Knowledge Distillation Model

Li, Mingyong; Tang, Lan; Peng, Shuang; Ma, Yuanliang; Yang, Degang

doi:10.1155/2021/5107034

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised hashing accomplishes the generation of hash codes by mining inter-and intra-modal correlations of different modalities without the assistance of label semantics [11,44,14,52,30]. Representative works include MGCMH [47] integrates multigraph learning and hash function learning into a joint framework using an unsupervised learning paradigm to uniformly map data from different modalities into the same hash space.…”

Section: Ii1 Unsupervised Cross-modal Hashingmentioning

confidence: 99%

Adaptive Asymmetric Label-guided Hashing for Multimedia Search

Long¹

2022

Preprint

View full text Add to dashboard Cite

With the rapid growth of multimodal media data on the Web in recent years, hash learning methods as a way to achieve efficient and flexible cross-modal retrieval of massive multimedia data have received a lot of attention from the current Web resource retrieval research community. Existing supervised hashing methods simply transform label information into pairwise similarity information to guide hash learning, leading to a potential risk of semantic error in the face of multi-label data. In addition, most existing hash optimization methods solve NP-hard optimization problems by employing approximate approximation strategies based on relaxation strategies, leading to a large quantization error. In order to address above obstacles, we present a simple yet efficient Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search. Specifically, A2LH is a two-step hashing method. In the first step, we design an association representation model between the different modality representations and semantic label representation separately, and use the semantic label representation as an intermediate bridge to solve the semantic gap existing between different modalities. In addition, we present an efficient discrete optimization algorithm for solving the quantization error problem caused by relaxation-based optimization algorithms. In the second step, we leverage the generated hash codes to learn the hash mapping functions. The experimental results show that our proposed method achieves optimal performance on all compared baseline methods.

show abstract

Section: Ii1 Unsupervised Cross-modal Hashingmentioning

confidence: 99%

Adaptive Asymmetric Label-guided Hashing for Multimedia Search

Long¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

mentioning

confidence: 99%

Retracted: Deep Unsupervised Hashing for Large‐Scale Cross‐Modal Retrieval Using Knowledge Distillation Model

Intelligence and Neuroscience

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h

Jiang,

Sun,

Wang

et al. 2024

Eur Radiol

View full text Add to dashboard Cite

Objectives We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. Methods ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. Results A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). Conclusions The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. Clinical relevance statement Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. Key Points • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

show abstract

[Retracted] Deep Unsupervised Hashing for Large‐Scale Cross‐Modal Retrieval Using Knowledge Distillation Model

Cited by 5 publications

References 34 publications

Adaptive Asymmetric Label-guided Hashing for Multimedia Search

Adaptive Asymmetric Label-guided Hashing for Multimedia Search

Retracted: Deep Unsupervised Hashing for Large‐Scale Cross‐Modal Retrieval Using Knowledge Distillation Model

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h

Contact Info

Product

Resources

About