PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation

Daliparthi, Venkata Satya Sai Ajay

doi:10.48550/arxiv.2109.10083

Cited by 1 publication

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“…Next, the dense flow branch will be described in detail. Due to the repeated convolutions and down-sampling operations, it may lead to the loss of details, and the deep layer of CNN cannot fully capture efficient contextual information of features [46]. Inspired by the idea of the dual-branch network [47], we introduce a lightweight dense flow branch to further enrich the high-level semantic information, as shown in Figure 4.…”

Section: Cnn Dual-branch Encodermentioning

confidence: 99%

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Adaptive learning Unet‐based adversarial network with CNN and transformer for segmentation of hard exudates in diabetes retinopathy

Zhang

et al. 2023

IET Image Processing

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Accurate segmentation of hard exudates in early non‐proliferative diabetic retinopathy can assist physicians in taking appropriate treatment in a more targeted manner, in order to avoid more serious damage to vision caused by the deterioration of the disease in the later stages. Here, an Adaptive Learning Unet‐based adversarial network with Convolutional neural network and Transformer (CT‐ALUnet) is proposed for automatic segmentation of hard exudates, combining the excellent local modelling ability of Unet with the global attention mechanism of transformer. Firstly, multi‐scale features are extracted through a CNN dual‐branch encoder. Then, the information fusion of features at adjacent scale is realized and the fused features are selected adaptively to maintain the overall consistency of features by attention‐guided multi‐scale fusion blocks (AGMFB). After that, the high‐level encoded features are input to transformer blocks to extract global contexts. Finally, these features are fused layer‐by‐layer to achieve accurate segmentation of hard exudates. In addition, adversarial training is incorporated into the above segmentation model, which improves Dice scores and MIoU scores by 7.5% and 3%, respectively. Experiments demonstrate that CT‐ALUnet shows more reliable segmentation and stronger generalization ability than other SOTA methods, which lays a good foundation for computer‐assisted diagnosis and assessment of efficacy.

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Section: Cnn Dual-branch Encodermentioning

confidence: 99%

“…We compare our proposed CT-ALUnet with seven methods based on deep learning: DeeplabV3+ [53], PDFnet [46], Unet++ [16], TransUnet [26], Hednet+cGAN [38], Bin [9], and…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Adaptive learning Unet‐based adversarial network with CNN and transformer for segmentation of hard exudates in diabetes retinopathy

Zhang

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation

Cited by 1 publication

References 0 publications

Adaptive learning Unet‐based adversarial network with CNN and transformer for segmentation of hard exudates in diabetes retinopathy

Adaptive learning Unet‐based adversarial network with CNN and transformer for segmentation of hard exudates in diabetes retinopathy

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