首页/论文/1912.12570

arXiv 1912.125702020-03-28cs.CV

Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention

Northeastern University（东北大学）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）

Zhihao Lei, Lin Qi, Ying Wei, Yunlong Zhou

备注：: 5 pages,5 figures

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

In this paper, we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation. More precisely, we added two modules based on 3D-UNet to better model information at different levels and locations. The dilated convolution pyramid downsampling module is mainly to solve the problem of loss of spatial information on the downsampling process, and it can effectively save details while reducing the resolution. The self-attention module can integrate the remote dependence on the feature maps in two dimensions of spatial and channel, effectively improving the representation ability and discriminating ability of the model. Our results are compared to the winners of iseg2017's first evaluation, the DICE ratio of WM and GM increased by 0.7%, and CSF is comparable.In the latest evaluation of the iseg-2019 cross-dataset challenge,we achieve the first place in the DICE of WM and GM, and the DICE of CSF is second.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Isointense infant brain MRI segmentation with a dilated convolutional neural network

Eindhoven University of Technology（埃因霍温理工大学）

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

2017-08-09cs.CV

基于扩张集成网络的多尺度MRI重建

Multi-scale MRI reconstruction via dilated ensemble networks

The University of Queensland（昆士兰大学）

本文提出一种基于扩张卷积和密集残差连接的多尺度MRI重建网络，在保持分辨率的同时高效捕获全局与局部特征，并探索复值版本以利用相位信息，实值模型以三倍效率超越现有方法。

2023-11-30cs.CVeess.IV

DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation

University of Science, Ho Chi Minh City（胡志明市自然科学大学）；John von Neumann Institute, Ho Chi Minh City（胡志明市约翰·冯·诺依曼研究所）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）；University of Arkansas, Fayetteville（阿肯色大学费耶特维尔分校）

While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume. In this paper, we tackle those limitations by developing a new deep learning model, named DAM-AL, which contains two main contributions, i.e., dilated attention mechanism and hard-case attention loss. Our DAM-AL netw…

2021-12-27cs.CVeess.IV

MS-YOLO：一种用于准确高效血细胞检测的多尺度模型

MS-YOLO: A Multi-Scale Model for Accurate and Efficient Blood Cell Detection

本文提出基于YOLOv11的血细胞检测模型MS-YOLO，通过多尺度膨胀残差模块、动态跨路径特征增强模块和轻量自适应权重下采样模块提升重叠细胞与小目标检测能力，在CBC基准上mAP@50达97.4%，满足临床实时部署需求。

2025-09-22cs.CV

Appearance-Based Gaze Estimation Using Dilated-Convolutions

The Hong Kong University of Science and Technology（香港科技大学）

Appearance-based gaze estimation has attracted more and more attention because of its wide range of applications. The use of deep convolutional neural networks has improved the accuracy significantly. In order to improve the estimation accuracy further, we focus on extracting better features from eye images. Relatively large changes in gaze angles may result in relatively small changes in eye appearance. We argue that current architectures for gaze estimation may not be able to capture such small changes, as they apply multiple pooling layers or other downsampling layers so that the spatial re…

2019-03-18cs.CV

DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement

Shandong Normal University（山东师范大学）；Nanjing University of Science and Technology（南京理工大学）

Low-light image enhancement is a classical computer vision problem aiming to recover normal-exposure images from low-light images. However, convolutional neural networks commonly used in this field are good at sampling low-frequency local structural features in the spatial domain, which leads to unclear texture details of the reconstructed images. To alleviate this problem, we propose a novel module using the Fourier coefficients, which can recover high-quality texture details under the constraint of semantics in the frequency phase and supplement the spatial domain. In addition, we design a s…

2022-09-16cs.CVeess.IV

更多相关论文收起更多论文

On the iterative refinement of densely connected representation levels for semantic segmentation

Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）；Facebook AI Research（Facebook人工智能研究）

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segment…

2018-04-30cs.CV

TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition

Institution for Advanced Computer Studies（高级计算机研究所）；University of Maryland, College Park（马里兰大学帕克分校）

We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks. By stacking spatial and temporal convolutions repeatedly, TAN forms a deep hierarchical representation for capturing spatio-temporal information in videos. Since we do not apply 3D convolutions in each layer but only apply temporal aggregation blocks once after each spatial downsampling layer in the network, we significantly reduce the model complexity. The use of dilated convolutions at different resolutions of the network helps in aggregating multi-scale spatio-tempora…

2018-12-14cs.CV

Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars

Vellore Institute of Technology（韦洛尔理工学院）

In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in downsampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention module is proposed to encode more contextual information and enhance the receptive field of the network. We present an in depth theoretical analysis of our network with training and optimization details. Our network was trained and tested on the…

2020-09-30cs.CVcs.LG

HC-Mamba：采用混合卷积技术的 Vision MAMBA 用于医学图像分割

HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation

School of Science（理学院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））

本文提出基于 Mamba 的医学图像分割模型 HC-Mamba，融合空洞卷积与深度可分离卷积，在降低计算成本的同时保留上下文信息，并在 Synapse、ISIC17 和 ISIC18 上验证其竞争力。

2024-10-02cs.CVeess.IV

重新思考基于Transformer的盲点网络用于自监督图像去噪

Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising

Junyi Li, Zhilu Zhang, and Wangmeng Zuo（李俊毅、张志鲁和祖旺萌）

本文针对自监督图像去噪中Transformer注意力违反盲点要求的问题，重新设计通道分组注意力与空间掩码注意力，提出TBSN，并通过知识蒸馏提升效率，在真实数据集上取得领先性能。

2025-08-19cs.CVeess.IV

PUCA：基于补丁反洗牌与通道注意力的增强自监督图像去噪

PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

Seoul National University（首尔国立大学）；Ewha Womans University（梨花女子大学）

针对自监督图像去噪中J不变性与架构灵活性受限的问题，提出PUCA，一种结合补丁反洗牌/洗牌和扩张注意力块的J不变U-Net架构，实现了最先进的去噪性能。

2023-10-16cs.CVcs.LGeess.IV