CLU-CNNs: Object detection for medical images

Li, Zhuoling; Dong, Minghui; Wen, Shiping; Hu, Xiang; Zhou, Pan; Zeng, Zhigang

doi:10.1016/j.neucom.2019.04.028

Cited by 149 publications

(59 citation statements)

References 9 publications

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“…Simple addition will blur the attention information, but channel fusion will get more attention features. Specific operations are as shown in Equations (8) and (9).…”

Section: Multi-scale Convolutional Model Based On Multiple Attentionmentioning

confidence: 99%

“…In recent years, deep learning has made a number of breakthroughs in the fields of computer vision [1,2], natural language processing [3,4], and speech recognition [5,6]. As one of the most typical deep learning models, convolutional neural networks (CNNs) have made considerable progress in image classification [7,8], object detection [9,10], image retrieval [11,12], and other applications. With the richness of image datasets and the improvement of machine performance, CNN's powerful feature extraction and generalization capabilities are increasingly favored by the industry.…”

Section: Introductionmentioning

confidence: 99%

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A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Yang

Feng

et al. 2019

Applied Sciences

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Computer vision systems are insensitive to the scale of objects in natural scenes, so it is important to study the multi-scale representation of features. Res2Net implements hierarchical multi-scale convolution in residual blocks, but its random grouping method affects the robustness and intuitive interpretability of the network. We propose a new multi-scale convolution model based on multiple attention. It introduces the attention mechanism into the structure of a Res2-block to better guide feature expression. First, we adopt channel attention to score channels and sort them in descending order of the feature's importance (Channels-Sort). The sorted residual blocks are grouped and intra-block hierarchically convolved to form a single attention and multi-scale block (AMS-block). Then, we implement channel attention on the residual small blocks to constitute a dual attention and multi-scale block (DAMS-block). Introducing spatial attention before sorting the channels to form multi-attention multi-scale blocks(MAMS-block). A MAMS-convolutional neural network (CNN) is a series of multiple MAMS-blocks. It enables significant information to be expressed at more levels, and can also be easily grafted into different convolutional structures. Limited by hardware conditions, we only prove the validity of the proposed ideas through convolutional networks of the same magnitude. The experimental results show that the convolution model with an attention mechanism and multi-scale features is superior in image classification.Appl. Sci. 2020, 10, 101 2 of 18 bottom-up models [16-18] and task-driven top-down models [19][20][21]. The research on the combination of deep learning and attention mechanisms has received considerable attention [22][23][24]. The goal of this study was to select information from the input that was relatively important to the current task. In deep neural networks, researchers often use masks to achieve attention. They demarcate key features in the data by training additional multi-layer weights. This approach naturally embeds the attention mechanism into the deep network structure and participates in end-to-end training. Attention models are well suited for solving computer vision tasks such as image classification, saliency analysis, and object detection.The same object will show different shapes in different natural scenes. When a computer vision system senses an unfamiliar scene, it cannot predict the scale of the object in the image in advance. Therefore, it is necessary to observe image information at different scales. The multi-scale representation of images can be divided into two types: Multi-scale space and multi-resolution pyramid. The difference between them is that multi-scale space has the same resolution at diverse scales. In the visual tasks, the multi-scale approach of multi-resolution pyramid processing targets can be separated into two categories: Image pyramid and feature pyramid. The image pyramid works best but the time and space complexity is high, and the feature pyramid is widely us...

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“…Simple addition will blur the attention information, but channel fusion will get more attention features. Specific operations are as shown in Equations (8) and (9).…”

Section: Multi-scale Convolutional Model Based On Multiple Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Yang

Feng

et al. 2019

Applied Sciences

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“…Pretata used a novel feature and a dimensionality reduction strategy to predict TATA binding proteins, and it achieved 92.92% prediction accuracy (Zou et al, 2016). Machine learning was also used to combine support vector machine (SVM) and PSSM distance transformation to identify DNA-binding proteins (Xu et al, 2015;Dong et al, 2019;Li Z. L. et al, 2019;Yan et al, 2019). Zou et al, proposed a model using a SVM named AOPs-SVM to identify antioxidant proteins (Jin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

Hou

Wang

2020

Front. Genet.

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ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.

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“…For example, Fan and Shao [2] investigated the positive almost periodic solutions for shunting inhibitory cellular neural networks with time-varying and continuously distributed delays, Li and Wang [3] analyzed the existence and exponential stability of the almost periodic solutions of shunting inhibitory cellular neural networks on time scales, Xia et al [4] established the sufficient conditions for the existence and exponential stability of almost periodic solution for shunting inhibitory cellular neural networks with impulses, Peng and Wang [5] addressed the existence and exponential stability of antiperiodic solutions to shunting inhibitory cellular neural networks with time-varying delays in leakage terms. For more related work on shunting inhibitory cellular neural networks, one can see [4,[6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Almost Automorphic Solutions to Cellular Neural Networks With Neutral Type Delays and Leakage Delays on Time Scales

Xu¹,

Liao²,

Li³

et al. 2020

IJCIS

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In this paper, cellular neural networks (CNNs) with neutral type delays and time-varying leakage delays are investigated. By applying the existence of the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem and the theory of calculus on time scales, a set of sufficient conditions which ensure the existence and exponential stability of almost automorphic solutions of the model are obtained. An example with its numerical simulations is given to support the theoretical findings.

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CLU-CNNs: Object detection for medical images

Cited by 149 publications

References 9 publications

A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

Almost Automorphic Solutions to Cellular Neural Networks With Neutral Type Delays and Leakage Delays on Time Scales

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