PKDN: Prior Knowledge Distillation Network for bronchoscopy diagnosis

Yan, Pengfei; Sun, Weiling; Li, Xiang; Li, Minglei; Jiang, Yuchen; Luo, Hao

doi:10.1016/j.compbiomed.2023.107486

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…Nonetheless, these feature extraction-based generative methods might result in the loss of crucial information while reducing data dimensionality. 27 In recent years, due to artificial intelligence technology's rapid development, 28 generative methods based on deep learning have gained significant prominence. 29 The deep learning-based generative techniques have found extensive utility across diverse domains, including image generation, style transfer, and highresolution reconstruction.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In recent years, due to artificial intelligence technology’s rapid development, generative methods based on deep learning have gained significant prominence . The deep learning-based generative techniques have found extensive utility across diverse domains, including image generation, style transfer, and high-resolution reconstruction. − Notably, virtual sample generation methods based on deep learning have garnered substantial interest among researchers and currently stand as a focal point of research.…”

Section: Introductionmentioning

confidence: 99%

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RegGAN: A Virtual Sample Generative Network for Developing Soft Sensors with Small Data

Wang,

Yan

2024

ACS Omega

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Quality variables play a pivotal role in monitoring the performance of chemical production systems. However, certain critical quality variables cannot be measured online through instruments. In such scenarios, using soft sensors becomes imperative to enable real-time measurements, accurately reflecting the system's operational status. The development of highperformance soft sensors requires abundantly labeled samples. Nevertheless, the prolonged periods and substantial costs associated with acquiring quality variable data pose challenges in obtaining sufficient labeled samples. Therefore, this paper proposes a regression generative adversarial network to generate virtual samples. The proposed method considers the mapping relationship between auxiliary and target variables while learning the data distribution. Moreover, the importance-weighted autoencoder is introduced to enhance the training stability of the generative model. The virtual samples, selected by using the similarity measurement algorithm, are incorporated into the training set. This inclusion addresses the diminished predictive performance of soft sensors when labeled samples are insufficient. The soft sensor employed in the anaerobic digestion process serves as a case study to illustrate the efficacy of the proposed generative method. Experimental results validate that the virtual samples generated by the proposed method exhibit greater proximity to the actual samples compared to those of other methods. Furthermore, integrating virtual samples into the training process of the long short-term memory-based soft sensor yields a 21.03% reduction in root-meansquare error compared with that of using the original training set alone.

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Section: ■ Introductionmentioning

confidence: 99%