57 citations · 69 across the 16 of their papers we have counts for
16 papers
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation
Huiyu Li, Xiabi Liu, Said Boumaraf +3
Small object segmentation, like tumor segmentation, is a difficult and critical task in the field of medical image analysis. Although deep learning based methods have achieved prom…
LCCo: Lending CLIP to Co-Segmentation
Xin Duan, Yan Yang, Liyuan Pan +1
This paper studies co-segmenting the common semantic object in a set of images. Existing works either rely on carefully engineered networks to mine the implicit semantic informatio…
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning
Xin Duan, Xiabi Liu, Xiaopeng Gong +1
This paper proposes an automatic image co-segmentation algorithm based on deep reinforcement learning (RL). Existing co-segmentation tasks mainly rely on deep learning methods, and…
Generating meta-learning tasks to evolve parametric loss for classification learning
Zhaoyang Hai, Xiabi Liu, Yuchen Ren +1
The field of meta-learning has seen a dramatic rise in interest in recent years. In existing meta-learning approaches, learning tasks for training meta-models are usually collected…
Mining the Weights Knowledge for Optimizing Neural Network Structures
Mengqiao Han, Xiabi Liu, Zhaoyang Hai +1
Knowledge embedded in the weights of the artificial neural network can be used to improve the network structure, such as in network compression. However, the knowledge is set up by…
A Novel Structured Natural Gradient Descent for Deep Learning
Weihua Liu, Xiabi Liu
Natural gradient descent (NGD) provided deep insights and powerful tools to deep neural networks. However the computation of Fisher information matrix becomes more and more difficu…
A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation
Xi Liu, Xiabi Liu, Huiyu Li +1
Image co-segmentation has attracted a lot of attentions in computer vision community. In this paper, we propose a new approach to image co-segmentation through introducing the dens…
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks
Mengqiao Han, Xiabi Liu, Zhaoyang Hai +1
Sparse neural networks are important for achieving better generalization and enhancing computation efficiency. This paper proposes a novel learning approach to obtain sparse fully…
Improving Image co-segmentation via Deep Metric Learning
Zhengwen Li, Xiabi Liu
Deep Metric Learning (DML) is helpful in computer vision tasks. In this paper, we firstly introduce DML into image co-segmentation. We propose a novel Triplet loss for Image Segmen…
Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis
Songxiao Yang, Xiabi Liu, Zhongshu Zheng +2
Current Computer-Aided Diagnosis (CAD) methods mainly depend on medical images. The clinical information, which usually needs to be considered in practical clinical diagnosis, has…
Evolving parametrized Loss for Image Classification Learning on Small Datasets
Zhaoyang Hai, Xiabi Liu
This paper proposes a meta-learning approach to evolving a parametrized loss function, which is called Meta-Loss Network (MLN), for training the image classification learning on sm…
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
Weihua Liu, Xiabi Liua, Xiongbiao Luo +4
This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is r…
Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality
Weihua Liu, Xiabi Liu, Murong Wang +1
The popular softmax loss and its recent extensions have achieved great success in the deep learning-based image classification. However, the data for training image classifiers usu…
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Said Boumaraf, Xiabi Liu, Chokri Ferkous +1
Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Im…
Automatic Image Co-Segmentation: A Survey
Xiabi Liu, Xin Duan
Image co-segmentation is important for its advantage of alleviating the ill-pose nature of image segmentation through exploring the correlation between related images. Many automat…
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
Huiyu Li, Xiabi Liu, Said Boumaraf +3
Automatic segmentation of liver tumors in medical images is crucial for the computer-aided diagnosis and therapy. It is a challenging task, since the tumors are notoriously small a…