Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Hu, Yang; Wen, Gongjian; Liao, Huiqiang; Wang, Changjun; Dai, Dawei; Yu, Zhiwen

doi:10.1109/tcyb.2019.2909925

Cited by 38 publications

(20 citation statements)

References 48 publications

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“…Tongue image modeling methods using color features [45], color and texture features [34], comprehensive features on color, texture, and geometric [52], we use support vector machine (SVM) as the classifier, and bayesian network classifier as in [34]; 2. The CNN to model after capturing the crack [51] and tooth-marked [27] features, the Dual-pipeline CNN model [19], and TongueNet [56]. For Tongue-Net, we removed the image segmentation interface and replaced it with the sigmoid classifier same as the proposed approach.…”

Section: Compare With the Other Tongue Modeling Methodsmentioning

confidence: 99%

“…The researches above rely on expert features, which is not automatic enough and is not conducive to generalization. The deep learning method can automatically extract the features of tongue image, [29,19] herbal prescription generation, based on tongue image. However, these studies only directly run the routine CNN models, ignoring the detailed attentional features of tongue images.…”

Section: Related Workmentioning

confidence: 99%

“…Tongue image modeling based on CNNs. Convolutional networks have been successfully applied in medical image modeling [19,11,36,13], CNN model ℱ cnn (⋅, W k ) extracts the visual features C(X) from input X tg , as:…”

Section: Overall Frameworkmentioning

confidence: 99%

“…The convolutional neural networks (CNNs) are widely applied in computer vision [30,36], include the modeling of tongue images [29,19]. However, there is no application of tongue-based disease location recognition.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fully-channel regional attention network for disease-location recognition with tongue images

Wen

Luo

et al. 2021

Artificial Intelligence in Medicine

Self Cite

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Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model. Methods: To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism. Results: The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition. Conclusion:In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency. Significance: The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications.

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Section: Compare With the Other Tongue Modeling Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Overall Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fully-channel regional attention network for disease-location recognition with tongue images

Wen

Luo

et al. 2021

Artificial Intelligence in Medicine

Self Cite

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show abstract

“…In [16], it also provides a meaningful TCM dataset. Hu et al [17] propose a dual-CNN and latent Dirichlet allocation (LDA) based model to construct a prescription from tongue images, which is consisted of two separated CNNs where the smaller CNN is regarded as auxiliary therapy topic feature extractor and trained by the multi-task learning strategy. However, in this form of prescription generation, no sequential information between herbs and symptoms are taken into account which is important in TCM prescription generation as described in our work and [18].…”

Section: Introductionmentioning

confidence: 99%

AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

et al. 2019

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In this paper, we propose a novel intelligent model, called AttentiveHerb, for simulating the doctor's inquiry and prescription that is composed by a series of herbs. It can automatically simulate some principles and learns the interaction between symptoms and herbs from clinical records of traditional herbal medicine. This model consists of two different attention mechanisms for distinguishing the main symptoms and paying different attention to different symptoms. By experiments, in terms of the predicted prescriptions, 51% of the total cases are in full accordance with the labels; in 1.09% of cases, all herbs of a label can be found in the predicted prescription and the predicted prescription have other additional herbs; in 15.4% of cases, all herbs of a predicted prescription can be found in their corresponding label; in 22.41% of cases, several herbs in each predicted prescription overlap with its label; and 10.1% of cases are completely different from the label. In summary, 67.49% of the predicted prescriptions are close to the labels, and 89.9% contain the same herbs with the labels, which indicates that the prescriptions generated by our model are close to those by doctors. Besides, our model can recommend herbs that do not appear in the label prescriptions but are useful for relieving symptoms. It shows that our model can learn some interactions between herbs and symptoms. With enough normalized traditional herbal medical records, this model works more accurately. This study also provides a benchmark for the upcoming researches in intelligent inquiry and prescription generation of traditional herbal medicine.

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Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Cited by 38 publications

References 48 publications

Fully-channel regional attention network for disease-location recognition with tongue images

Fully-channel regional attention network for disease-location recognition with tongue images

AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

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