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ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
[article]
2020
arXiv
pre-print
Preserved Fulltext
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification ...
In this paper, we propose a novel deep neural network architecture, namely Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. ...
[20] proposed an augmented technique for the R-CNN algorithm with a context model and small region proposal generator; which was the first benchmark dataset for small object detection. ...
arXiv:2009.12894v1
fatcat:dudddq2nmzdt7gkq2bpiyv2yfu
Fine-Context Shadow Detection using Shadow Removal
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.09609v1 fatcat:wqetjsqryzcn3c3me3lriwufcq
First, we propose a Fine Context-aware Shadow Detection Network (FCSD-Net), where we constraint the receptive field size and focus on low-level features to learn fine context features better. ...
To make use of this complementary nature of shadow detection and removal tasks, we train an auxiliary network for shadow removal and propose a complementary feature learning block (CFL) to learn and fuse ...
[15] explored a direction-aware manner to analyze image context for shadow detection. ...
arXiv:2109.09609v2
fatcat:v2tmamsmyjho5ergimxumah5gq
Stan: Small tumor-aware network for breast ultrasound image segmentation
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. ...
The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. ...
To overcome this problem, we propose the Small Tumor-Aware Network (STAN) to extract and fuse image context information at different scales. ...
arXiv:2002.01034v1
fatcat:vmy5hfg3yvg6vaf4hba37lsebq
TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation
[article]
2020
arXiv
pre-print
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To this end, we propose a three-stream boundary-aware network (TB-Net). ...
Existing methods mainly address the tasks of crack detection and segmentation that are only tailored for long-thin crack disease. ...
Results of the ablation experiments for analyzing the effect of the context-aware attention module. We compare our TB-Net to the model that does not make use of the context-aware attention module. ...
arXiv:2011.03703v1
fatcat:stfwqimr25c57gvfz7e4qctzke
Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection
2022
Frontiers in Plant Science
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The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. ...
The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. ...
Generally, low-level features are beneficial for the detection of small objects. ...
doi:10.3389/fpls.2022.895944
pmid:35720529
pmcid:PMC9201688
fatcat:ku32czhnp5fy3nwvj6icppqb7q
ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection
[article]
2021
arXiv
pre-print
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Finally, Mask-Aware Multi-Attention detects densely arranged and small acne by suppressing uninformative regions and highlighting probable acne regions. ...
To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention ...
Third, Mask-Aware Multi-Attention suppresses noise and highlights object cues for densely distributed and small acne detection. ...
arXiv:2105.14891v3
fatcat:j64nn46o4zgcbisezorz6wiuze
ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images
2020
Remote Sensing
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As a result, more relevant and accurate context information is extracted for further detection. ...
Object detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. ...
[15] proposed a context-aware detection network (CAD-Net) to integrate scene-level global semantics and object-level local contexts of objects for more consideration of low-contrast objects. ...
doi:10.3390/rs12244027
fatcat:jxhuwbvyx5fblkdkqpq55zjauu
COMET: Context-Aware IoU-Guided Network for Small Object Tracking
[article]
2020
arXiv
pre-print
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To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy. ...
Empirically, COMET outperforms the state-of-the-arts in a range of aerial view datasets that focusing on tracking small objects. ...
Motivated by the issues and also recent advances in small object detection, this paper proposes a Context-aware iOu-guided network for sMall objEct Tracking (COMET). ...
arXiv:2006.02597v3
fatcat:m2jmlmdryvbplfv6xkhgm6jxzi
Scale-Aware Trident Networks for Object Detection
[article]
2019
arXiv
pre-print
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Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. ...
In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. ...
for object detection. ...
arXiv:1901.01892v2
fatcat:mb5n2jo5zngoxbspmba4oydnha
SAC-Net: Spatial Attenuation Context for Salient Object Detection
[article]
2020
arXiv
pre-print
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By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects. ...
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. ...
Moreover, we compare the time performance of our SAC-Net with other methods for salient object detection. ...
arXiv:1903.10152v3
fatcat:rfd5v5basjgl7fqs7rqoupt7fy
EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network
[article]
2020
arXiv
pre-print
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To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline ...
Therefore, the centerline detection sub-network is able to enhance the accuracy and robustness of segmentation task, especially when only a small set of annotated data are available. ...
The performance of both our method and 3D U-Net are superior than the 2D U-Net, which further confirms the importance of 3D spatial context. ...
arXiv:1912.00201v3
fatcat:o6r3a32jdbeojeslks27aclmaq
Camouflaged Object Detection via Context-aware Cross-level Fusion
[article]
2022
arXiv
pre-print
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To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. ...
Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. ...
For the small polyps, C 2 F-Net can accurately detect boundaries, while UNet [48] and UNet++ [68] fail. ...
arXiv:2207.13362v1
fatcat:vz5zdjbzpvd7da5a2lwdzq2anm
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
[article]
2019
arXiv
pre-print
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In addition, to regularize the global semantic context, we implemented a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted ...
Experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated the superiority of the proposed method for point cloud classification. ...
ACKNOWLEDGMENTS We would like to gratefully acknowledge the ISPRS for providing airborne LiDAR data. ...
arXiv:1910.05909v1
fatcat:pfozwk24nbhzpjc3smjcusoc6i
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
2021
Applied Sciences
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Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. ...
with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule ...
As shown, the Optimized Faster R-CNN model is able to detect the nodule regions, even for small nodules. ...
doi:10.3390/app112110132
fatcat:ohptudd47ffldp5o65a77w4v6i
A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation
[article]
2022
arXiv
pre-print
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, and a global context block (GCB) to refine features. ...
SE uses a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to boost small ship performance, a feature balance operation (FBO) to improve scale feature description ...
For better multi-scale performance, based on FPN [22] , SE employs a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to enhance small ship performance, a ...
arXiv:2207.03912v1
fatcat:hvvlcaiavnfhhkqn4g7qeic5ie
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