Attention Unet++: A Nested Attention-Aware U-Net for Liver CT Image Segmentation

Li, Chen; Tan, Yusong; Chen, Wei; Luo, Xin; Gao, Yuanming; Jia, Xiaogang; Wang, Zhiying

doi:10.1109/icip40778.2020.9190761

Cited by 103 publications

(53 citation statements)

References 5 publications

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“…U-NET deep FCN structure is highly applicable for medical image segmentation. Multiple U-NET variants [ 41 – 43 ] and domain specific models [ 44 ] have been applied to process medical images. For instance, [ 41 ] presents a U-Net variant for image segmentation on brain tumor MRI scans while [ 42 ] presents another U-Net variant based on nested and dense skip connections for medical image segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, [ 43 ] introduces a robust self-adapting U-Net-based framework for medical image segmentation. And [ 44 ] adds the emerging attention mechanism to a nested U-Net architecture for image segmentation on liver CT scans. One interesting medical application of image segmentation using a deep learning model is presented in [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

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A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

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Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

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“…Also, attention mechanism has been widely in various tasks recently wherever selective features of most importance at the task are to be weighed more suppressing other complex irrelevant features. For example in [14] attention mechanism was used in U-Net++ for automatic segmentation of liver by merging only task oriented features at different levels in the encoder-decoder architecture. This works due to the ability of attention mechanism in increasing the weight of the focus regions while suppressing the regions in background that are unrelated to the segmentation task at hand.…”

Section: Introductionmentioning

confidence: 99%

Attention Based Multi-Patched 3D-CNNs with Hybrid Fusion Architecture for Reducing False Positives during Lung Nodule Detection

Vipparla¹,

Chilukuri²,

Kande³

2021

JCC

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In lung nodules there is a huge variation in structural properties like Shape, Surface Texture. Even the spatial properties vary, where they can be found attached to lung walls, blood vessels in complex non-homogenous lung structures. Moreover, the nodules are of small size at their early stage of development. This poses a serious challenge to develop a Computer aided diagnosis (CAD) system with better false positive reduction. Hence, to reduce the false positives per scan and to deal with the challenges mentioned, this paper proposes a set of three diverse 3D Attention based CNN architectures (3D ACNN) whose predictions on given low dose Volumetric Computed Tomography (CT) scans are fused to achieve more effective and reliable results. Attention mechanism is employed to selectively concentrate/weigh more on nodule specific features and less weight age over other irrelevant features. By using this attention based mechanism in CNN unlike traditional methods there was a significant gain in the classification performance. Contextual dependencies are also taken into account by giving three patches of different sizes surrounding the nodule as input to the ACNN architectures. The system is trained and validated using a publicly available LUNA16 dataset in a 10 fold cross validation approach where a competition performance metric (CPM) score of 0.931 is achieved. The experimental results demonstrate that either a single patch or a single architecture in a one-to-one fashion that is adopted in earlier methods cannot achieve a better performance and signifies the necessity of fusing different multi patched architectures. Though the proposed system is mainly designed for pulmonary nodule detection it can be easily extended to classification tasks of any other 3D medical

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“…Li et al (2020) [ 33 ] demonstrated the application of a nested attention-aware U-Net on CT images for the goal of liver segmentation. The authors concluded that the proposed novel method achieved competitive performances on the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

Šegota

Lorencin

Smolić

et al. 2021

Biology

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Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.

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Attention Unet++: A Nested Attention-Aware U-Net for Liver CT Image Segmentation

Cited by 103 publications

References 5 publications

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Attention Based Multi-Patched 3D-CNNs with Hybrid Fusion Architecture for Reducing False Positives during Lung Nodule Detection

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

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