AOGNets: Compositional Grammatical Architectures for Deep Learning

Li, Xilai; Song, Xi; Wu, Tianfu

doi:10.1109/cvpr.2019.00638

Cited by 21 publications

(14 citation statements)

References 49 publications

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“…Some recent substantial models have been developed based on the base models, such as Res2Net [83] and Wide ResNet [84] using the ResNet model; while Log Dense Net [85] and Sparse Net [86] using the DenseNet model. Another path of development combining multiple base models has resulted in a number of hybrid models including AOGNet [87] , PNASNet [88] , AmoebaNet [89] , DPN [90] , HCGNet [76] , GCNet [91] , ThiNet [92] , and SKNet [93] etc.…”

Section: Model Developmentmentioning

confidence: 99%

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

et al. 2020

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Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

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Section: Model Developmentmentioning

confidence: 99%

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

et al. 2020

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“…We have designed CNN models by the widely used strategy of ''stage-wise building-block'' [37]. As shown in Figure 2, the CNN model consists of several ''RANGE''s, each RANGE has some convolutional layers, and each convolutional layer is followed by a batch normalization layer and a leaky rectified linear activation layer (Leaky ReLU).…”

Section: Cnn Model Design and Parameters For Optimizationmentioning

confidence: 99%

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Feng³

et al. 2020

IEEE Access

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Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs' decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD. INDEX TERMS Chronic obstructive pulmonary disease (COPD), deep learning, convolutional neural networks, computed tomography (CT), airway, image classification.

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“…• Networks imbued with logical structure, e.g., probabilistic decision trees. By forcing the network to reason logically, we may be better able to understand its reasoning process (e.g., Wu and Song [63], Li, Song and Wu [64]).…”

Section: Interpretable ML As the Way Forward In Embryo Selectionmentioning

confidence: 99%

Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization

Afnan

Rudin

Conitzer

et al. 2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

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AI has the potential to revolutionize many areas of healthcare. Radiology, dermatology, and ophthalmology are some of the areas most likely to be impacted in the near future, and they have received significant attention from the broader research community. But AI techniques are now also starting to be used in in vitro fertilization (IVF), in particular for selecting which embryos to transfer to the woman. The contribution of AI to IVF is potentially significant, but must be done carefully and transparently, as the ethical issues are significant, in part because this field involves creating new people.We first give a brief introduction to IVF and review the use of AI for embryo selection. We discuss concerns with the interpretation of the reported results from scientific and practical perspectives. We then consider the broader ethical issues involved. We discuss in detail the problems that result from the use of black-box methods in this context and advocate strongly for the use of interpretable models. Importantly, there have been no published trials of clinical effectiveness, a problem in both the AI and IVF communities, and we therefore argue that clinical implementation at this point would be premature. Finally, we discuss ways for the broader AI community to become involved to ensure scientifically sound and ethically responsible development of AI in IVF.

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AOGNets: Compositional Grammatical Architectures for Deep Learning

Cited by 21 publications

References 49 publications

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization

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