CCF-Net: Composite Context Fusion Network with Inter-Slice Correlative Fusion for Multi-Disease Lesion Detection

Li, Zihao; Ma, Jiechao; Zhang, Shu; Shi, Yemin; Zhang, Junge; Huang, Kaiqi; Yu, Yizhou

doi:10.1109/icip42928.2021.9506563

2021 IEEE International Conference on Image Processing (ICIP) 2021

DOI: 10.1109/icip42928.2021.9506563

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CCF-Net: Composite Context Fusion Network with Inter-Slice Correlative Fusion for Multi-Disease Lesion Detection

Zihao Li

Jiechao Ma

Shu Zhang

et al.

Abstract: Detecting lesions from computed tomography (CT) scans relies on two aspects of the input: intra-slice texture information from the key slice and inter-slice structural context information from the adjacent slices. However, most existing methods ignore the correlation and complementarity between texture and structural information resulting in unexpected loss of performance. In this paper, a novel Composite Context Fusion Network (CCF-Net) is proposed to jointly model intra-slice and inter-slice features so as t… Show more

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Cited by 2 publications

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CeLNet: a correlation-enhanced lightweight network for medical image segmentation

et al. 2023

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Objective. Convolutional Neural Networks(CNN) have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. As the segmentation accuracy gets constantly updated, the complexity of networks increases as well. Complex networks can achieve better performance but require more parameters and are hard to train with limited resources, while lightweight models are faster but cannot fully utilize the contextual information of medical images. In this paper, we focus on better balancing the efficiency and accuracy. Approach. We propose a correlation-enhanced lightweight network (CeLNet) for medical image segmentation, which adopts a siamese structure for weight sharing and parameter saving. Through the feature reuse and feature stacking of parallel branches, a point-depth convolution parallel block (PDP Block) is proposed to reduce the model parameters and computational cost while improving the feature extraction capability of encoder. A relation module is also designed to extract feature correlations of input slices, which utilizes global and local attention to enhance feature connections, while reducing feature differences through element subtraction, and finally obtains contextual information of associated slices to improve the segmentation performance. Main results. We conduct extensive experiments on the LiTS2017, MM-WHS and ISIC2018 datasets, and the proposed model consumes merely 5.18M parameters but achieves excellent segmentation performance, specifically, a DSC of 0.9233 in LiTS2017 dataset, an average DSC of 0.7895 on MM-WHS dataset and an average DSC of 0.8401 on ISIC2018 dataset. Significance. CeLNet achieves state-of-the-art performance in multiple datasets while ensuring lightweight.

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CeLNet: a correlation-enhanced lightweight network for medical image segmentation

et al. 2023

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Kernel Quantization for Efficient Network Compression

Shi

2022

IEEE Access

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This paper presents a novel network compression framework, Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version without significant performance loss. Unlike existing methods struggling with weight bit-length, KQ has the potential in improving the compression ratio by considering the convolution kernel as the quantization unit. Inspired by the evolution from weight pruning to filter pruning, we propose to quantize in both kernel and weight level. Instead of representing each weight parameter with a low-bit index, we learn a kernel codebook and replace all kernels in the convolution layer with corresponding low-bit indexes. Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio. Then, we conduct a 6-bit parameter quantization on the kernel codebook to further reduce redundancy. Extensive experiments on the ImageNet classification task prove that KQ needs 1.05 and 1.62 bits on average in VGG and ResNet18, respectively, to represent each parameter in the convolution layer and achieves the state-of-the-art compression ratio with little accuracy loss.

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CCF-Net: Composite Context Fusion Network with Inter-Slice Correlative Fusion for Multi-Disease Lesion Detection

Cited by 2 publications

References 13 publications

CeLNet: a correlation-enhanced lightweight network for medical image segmentation

CeLNet: a correlation-enhanced lightweight network for medical image segmentation

Kernel Quantization for Efficient Network Compression

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