Search Results for author: Dandan Li

Found 8 papers, 1 papers with code

FedGraph: an Aggregation Method from Graph Perspective

no code implementations6 Oct 2022 Zhifang Deng, Xiaohong Huang, Dandan Li, Xueguang Yuan

The FedGraph takes three factors into account from coarse to fine: the proportion of each local dataset size, the topology factor of model graphs, and the model weights.

Federated Learning Tumor Segmentation

MISSFormer: An Effective Medical Image Segmentation Transformer

1 code implementation15 Sep 2021 Xiaohong Huang, Zhifang Deng, Dandan Li, Xueguang Yuan

The CNN-based methods have achieved impressive results in medical image segmentation, but it failed to capture the long-range dependencies due to the inherent locality of convolution operation.

Ranked #8 on Medical Image Segmentation on Synapse multi-organ CT (using extra training data)

Cardiac Segmentation Image Segmentation +2

Comb Convolution for Efficient Convolutional Architecture

no code implementations1 Nov 2019 Dandan Li, Yuan Zhou, Shuwei Huo, Sun-Yuan Kung

Convolutional neural networks (CNNs) are inherently suffering from massively redundant computation (FLOPs) due to the dense connection pattern between feature maps and convolution kernels.

C-DLSI: An Extended LSI Tailored for Federated Text Retrieval

no code implementations5 Oct 2018 Qijun Zhu, Dandan Li, Dik Lun Lee

Different from existing centralized information retrieval (IR) methods, in which search is done on a logically centralized document collection, FTR is composed of a number of peers, each of which is a complete search engine by itself.

Clustering Information Retrieval +2

Representing Sets as Summed Semantic Vectors

no code implementations24 Sep 2018 Douglas Summers-Stay, Peter Sutor, Dandan Li

Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures.

Hifi: Hierarchical feature integration for skeleton detection

no code implementations1 Jul 2018 Kai Zhao, Wei Shen, ShangHua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.

Object Object Skeleton Detection

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

no code implementations5 Jan 2018 Kai Zhao, Wei Shen, Shang-Hua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.

Object Object Skeleton Detection

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