ECAU-Net: Efficient channel attention U-Net for fetal ultrasound cerebellum segmentation

Shu, Xin; Chang, Feng Chi; Zhang, Xin; Shao, Changbin; Yang, Xibei

doi:10.1016/j.bspc.2022.103528

Cited by 17 publications

(7 citation statements)

References 31 publications

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“…16 (29%) studies reported the computational time, and 7 (13%) studies reported what kind of computational hardware was used. 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 Computation time ranged from 70 micro seconds to 25 min. 32 , 35 Next, we discuss the studies per topic in the following order: biometry, standard plane detection, segmentation, and together: growth models, abnormality detection, quality enhancement and visualization.…”

Section: Resultsmentioning

confidence: 99%

“… 38 , 40 , 68 , 69 , 70 Three of those used the aforementioned HC18 challenge dataset. 40 , 44 , 68 , 69 Furthermore, four studies segmented the cerebellum, 39 , 41 , 71 , 72 and three studies segmented the choroid plexus. 38 , 39 , 72 All these studies used a learning-based method, and four studies used data acquired in the first trimester (<14 weeks).…”

Section: Resultsmentioning

confidence: 99%

“…Of the 34 studies, 14 (41%) used data-augmentation. 23 , 32 , 33 , 37 , 40 , 41 , 45 , 47 , 50 , 61 , 64 , 65 , 68 , 69 Flipping and rotation were the most used in 71% and respectively 57% of the cases. Five (15%) studies used only cross-validation, 19 (56%) studies used only an external test set, 7 (20%) studies used both, and for three (9%) studies this was not mentioned.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

Bastiaansen¹,

Klein²,

Koning³

et al. 2023

eBioMedicine

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

Bastiaansen¹,

Klein²,

Koning³

et al. 2023

eBioMedicine

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“…Here, attention is computed for the neighboring k channels to achieve cross-channel information interactions. We follow the setup in [50] and [51] by making γ = 2 and b = 1 to ensure accuracy while introducing a smaller number of parameters. We filter the results of the multiscale pyramid module for channel importance by introducing the ECA channel attention mechanism with only a small number of parameters introduced.…”

Section: A Multiscale Pyramidal Pooling Network With Attention Mechan...mentioning

confidence: 99%

A Knowledge Distillation-Based Ground Feature Classification Network With Multiscale Feature Fusion in Remote-Sensing Images

Yang,

Wang,

Dong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As a fundamental task in remote-sensing interpretation, semantic segmentation of remote-sensing images intends to allocate a definite class to each pixel in the image. Fast and efficient semantic segmentation of high-resolution remote-sensing images provides help to capture the real surface covering and plays an essential role in urban planning and dynamic monitoring. However, there are still some limitations in the previous remote-sensing image semantic segmentation model for urban scenes, such as the low weight of small target pixels and the tiny target size leading to the unsatisfactory recognition and segmentation results of the model for small target features. Meanwhile, the deeper and broader feature extraction module in the semantic segmentation network usually leads to more redundant parameters, which takes a lot of computation time. Thus, we propose a lightweight semantic segmentation network based on the knowledge distillation combined with a multiscale pyramidal pooling module and attention mechanism named KD-MSANet, which enhanced the ability to fuse and focus on shallow features. Then, we trained teacher-student models to obtain lightweight network models through a model pruning and distillation framework. Experiments on Vaihingen and Potsdam datasets demonstrated that the network we designed significantly reduces the number of parameters while ensuring almost constant accuracy. Compared with the precompression model, the student model reduced in size by 43.6% and the training efficiency was improved by 22.3%, while the accuracy reached 99.30% of the teacher model.

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“…Therefore, improving the 3D object detection algorithm is of great significance to further enhance the performance and safety of autonomous driving technology. In this paper, for the problem of information loss and inaccurate localization due to the column division of the point cloud in the PointPillars algorithm, the Efficient Channel Attention module [18] is introduced to improve the original algorithm's 2D backbone network. And the contributions of our work are:…”

Section: Introductionmentioning

confidence: 99%

A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

Wang,

Chen

2023

Applied Sciences

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Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios.

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ECAU-Net: Efficient channel attention U-Net for fetal ultrasound cerebellum segmentation

Cited by 17 publications

References 31 publications

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

A Knowledge Distillation-Based Ground Feature Classification Network With Multiscale Feature Fusion in Remote-Sensing Images

A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

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