A multiscale dilated dense convolutional network for saliency prediction with instance-level attention competition

Li, Hao; Qi, Fei; Shi, Guangming; Lin, Chunhuan

doi:10.1016/j.jvcir.2019.102611

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…Likewise, up-sampling increases the receptive field but at the same time may negatively affect our capability to extract useful information about the local context. In order to keep the context information, which is essential, and decrease the ambiguity caused by local areas while keeping the number of parameters in the receptive fields constant, atrous convolution, also called dilated convolution, was proposed [ 33 ]. Atrous convolution was implemented via assigning zero values to the relevant weights of the filter.…”

Section: Automated Segmentation Of Myocardial Infarction: Myocardial ...mentioning

confidence: 99%

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Wang

Abdelaty

Parke

et al. 2023

Entropy

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Myocardial infarction (MI) occurs when an artery supplying blood to the heart is abruptly occluded. The “gold standard” method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no “gold standard” fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.

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Section: Automated Segmentation Of Myocardial Infarction: Myocardial ...mentioning

confidence: 99%

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Wang

Abdelaty

Parke

et al. 2023

Entropy

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“…Dilated convolution enlarges the effective receptive fields. It has been used to provide multi-scale representation in various network configurations [14,[18][19][20]. However, it does not solve the problem of scale completely by itself.…”

Section: Related Workmentioning

confidence: 99%

Adaptive convolution kernel for artificial neural networks

Tek

Cam

Karlı

2021

Journal of Visual Communication and Image Representation

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Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture.The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ''Faces in the Wild'' showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing ordinary convolution layers in a U-shaped network with 7 × 7 adaptive layers can improve its learning performance and ability to generalize.✩ This paper has been recommended for acceptance by Zicheng Liu.

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“…Visual saliency detection is a method by imitating human visual characteristics 13 for determining where objects or regions are most likely to attract human attention in images that has attracted increasing attention and shown great success in a variety of fields, including hyper-spectral anomaly detection, 14 saliency detection, 15 and real-time wood classification. 16 As fabric defects generally are salient in fabric images compared to the complex texture background, the fabric defect detection can be regarded as a salient object detection (SOD) problem, and thus several visual saliency-based fabric defect detection methods 17–19 have been developed and achieved good performance.…”

Section: Introductionmentioning

confidence: 99%

A context-aware progressive attention aggregation network for fabric defect detection

Liu

Tian

et al. 2023

Journal of Engineered Fibers and Fabrics

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Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multi-scale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets.

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A multiscale dilated dense convolutional network for saliency prediction with instance-level attention competition

Cited by 13 publications

References 26 publications

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Adaptive convolution kernel for artificial neural networks

A context-aware progressive attention aggregation network for fabric defect detection

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