Active disease-related compound identification based on capsule network

Yang, Bin; Bao, Wenzheng; Wang, Jinglong

doi:10.1093/bib/bbab462

Cited by 24 publications

(16 citation statements)

References 42 publications

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“…Symptoms of COVID-19 include nasal congestion, sore throat, diarrhea, and drowsiness, in addition to fever and cough. Critically ill patients may present with respiratory distress, acute respiratory distress syndrome (ARDS), organ failure, etc [ 3 ]. From the available epidemiological characteristics, researchers have explored that the main routes of COVID-19 transmission are droplet and close contact transmission with an incubation period of 1–14 days [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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“…Multiplication of the vectorized sub-matrices and the vectorized kernels are a product of two uncorrelated random vectors. Therefore, the resulting mean and covariance are given by Equations (7). Batch Normalization Layer:…”

Section: Max-pooling Operationmentioning

confidence: 99%

“…DNNs should be able to assess their own performance degradation and warn the users about their possible failure. For DNNs employed in modern healthcare systems, the inability to identify performance degradation may have fatal repercussions (3)(4)(5)(6)(7). Performance monitoring after deployment requires an accurate self-assessment mechanism since there is no ground truth available to track model accuracy anymore.…”

Section: Introductionmentioning

confidence: 99%

Failure Detection in Deep Neural Networks for Medical Imaging

Ahmed

Dera

Hassan

et al. 2022

Front. Med. Technol.

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Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of applications of DNNs in modern healthcare, their trustworthiness and reliability are becoming increasingly important. An essential aspect of trustworthiness is detecting the performance degradation and failure of deployed DNNs in medical settings. The softmax output values produced by DNNs are not a calibrated measure of model confidence. Softmax probability numbers are generally higher than the actual model confidence. The model confidence-accuracy gap further increases for wrong predictions and noisy inputs. We employ recently proposed Bayesian deep neural networks (BDNNs) to learn uncertainty in the model parameters. These models simultaneously output the predictions and a measure of confidence in the predictions. By testing these models under various noisy conditions, we show that the (learned) predictive confidence is well calibrated. We use these reliable confidence values for monitoring performance degradation and failure detection in DNNs. We propose two different failure detection methods. In the first method, we define a fixed threshold value based on the behavior of the predictive confidence with changing signal-to-noise ratio (SNR) of the test dataset. The second method learns the threshold value with a neural network. The proposed failure detection mechanisms seamlessly abstain from making decisions when the confidence of the BDNN is below the defined threshold and hold the decision for manual review. Resultantly, the accuracy of the models improves on the unseen test samples. We tested our proposed approach on three medical imaging datasets: PathMNIST, DermaMNIST, and OrganAMNIST, under different levels and types of noise. An increase in the noise of the test images increases the number of abstained samples. BDNNs are inherently robust and show more than 10% accuracy improvement with the proposed failure detection methods. The increased number of abstained samples or an abrupt increase in the predictive variance indicates model performance degradation or possible failure. Our work has the potential to improve the trustworthiness of DNNs and enhance user confidence in the model predictions.

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“…The model can effectively classify virus particle proteins, and the accuracy of the model is better than the traditional machine learning algorithm. Yang et al (2021) proposed a novel disease-related compound identification model based on the capsule network (CapsNet). The CapsNet medal was used to identify the pneumonia-related compounds in Qingre Jiedu injection.…”

Section: Introductionmentioning

confidence: 99%

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

Shi

et al. 2022

Front. Plant Sci.

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Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.

show abstract

Active disease-related compound identification based on capsule network

Cited by 24 publications

References 42 publications

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Failure Detection in Deep Neural Networks for Medical Imaging

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

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