DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation

IEEE Trans. Cogn. Dev. Syst.

et al. 2022

Self Cite

Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization in the case of using variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face are not well extracted. To address this issue, we propose a multi-modility data based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture that can selectively fuse lowlevel and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen discrimination ability of network models on physical attribute features than identity structure features under certain constraints. In addition, a multi-scale patch based weighted finetuning strategy is designed to learn each specific local face region. Experimental results show that the proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material masks spoofing.

Section: B Deep Learning For Face Anti-spoofingmentioning

confidence: 99%

Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing

Liu

Wei²,

IEEE Trans. Cogn. Dev. Syst.

et al. 2022

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“…LV-Net (Lei et al, 2020b) uses a lightweight network to segment the liver. Furthermore, there are some improved networks such as DefU-Net (Lei et al, 2021), CE-Net (Gu et al, 2019) and MSB-Net (Shao et al, 2019) that use multi-scale feature fusion to enhance the feature representation of the network.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Pooling Operation for Liver and Liver-Tumor Segmentations

Zhao

et al. 2022

Front. Signal Process.

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Deep convolutional neural networks (DCNNs) have been widely used in medical image segmentation due to their excellent feature learning ability. In these DCNNs, the pooling operation is usually used for image down-sampling, which can gradually reduce the image resolution and thus expands the receptive field of convolution kernel. Although the pooling operation has the above advantages, it inevitably causes information loss during the down-sampling of the pooling process. This paper proposes an effective weighted pooling operation to address the problem of information loss. First, we set up a pooling window with learnable parameters, and then update these parameters during the training process. Secondly, we use weighted pooling to improve the full-scale skip connection and enhance the multi-scale feature fusion. We evaluated weighted pooling on two public benchmark datasets, the LiTS2017 and the CHAOS. The experimental results show that the proposed weighted pooling operation effectively improve network performance and improve the accuracy of liver and liver-tumor segmentation.

“…In the past few years, U-Net [3] is one of the most popular network architectures in the field of medical image segmentation based on encoder-decoder networks. Numerous methods based on U-Net have been proposed to segment medical images accurately [4]. The most common way is to use the backbone of classic convolutional neural networks with pre-trained parameters such as VGG [5], ResNet [6] and DenseNet [7] etc.…”

Section: Introductionmentioning

confidence: 99%

Qau-Net: Quartet Attention U-Net for Liver and Liver-Tumor Segmentation

Hong

Wang

2021 IEEE International Conference on Multimedia and Expo (ICME)

et al. 2021

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U-Net and a large number of variants of U-Net have been successfully used for liver and liver-tumor segmentation. In this paper, we propose a novel network called quartet attention U-Net (QAU-Net).First, QAU-Net employs quartet attention including four branches to capture inner and cross-dimensional interactions between channels and spatial locations. Secondly, QAU-Net employs long-short skip-connection to instead of the vanilla skip-connection, which avoids the duplicate process of low-resolution information and improves the feature fusion of low-resolution and high-resolution information. We evaluate the proposed method on the public LITS dataset. Experiments demonstrate that QAU-Net has better feature representation and higher liver and liver-tumor segmentation accuracy.