scholarly journals Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Biao Jie ◽  
Mingxia Liu ◽  
Chunfeng Lian ◽  
Feng Shi ◽  
Dinggang Shen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Diagnosis ◽  
Correlation Kernels ◽  
Weighted Correlation

scholarly journals A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 219
Author(s):  
Mukhammed Garifulla ◽  
Juncheol Shin ◽  
Chanho Kim ◽  
Won Hwa Kim ◽  
Hye Jung Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Medical Devices ◽  
Energy Efficient ◽  
High Performance ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Mobile System ◽  
Performance Computing

Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.

scholarly journals How Convolutional Neural Networks Diagnose Plant Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Diagnosis ◽  
Black Box ◽  
Plant Diseases ◽  
Training Dataset ◽  
Great Success ◽  
Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

scholarly journals Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8668 ◽  
Author(s):  
Liangqun Lu ◽  
Bernie J. Daigle
Keyword(s):  
Hepatocellular Carcinoma ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Free Survival ◽  
Disease Diagnosis ◽  
Image Features ◽  
Biological Pathways ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Histopathological Images

Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models—VGG 16, Inception V3 and ResNet 50—to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E−18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E−07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways.

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