Semi-supervised medical image segmentation based on GAN with the pyramid attention mechanism and transfer learning

Li, Guoqin; Wang, Jin; Tan, Yanli; Shen, Lingyun; Jiao, Dongli; Zhang, Quan

doi:10.1007/s11042-023-16213-z

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-16213-z

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Semi-supervised medical image segmentation based on GAN with the pyramid attention mechanism and transfer learning

Guoqin Li,

Jin Wang,

Yanli Tan

et al.

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2024

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Cited by 3 publications

References 33 publications

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GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging

Biswas,

Maity,

Banik

et al. 2024

SN COMPUT. SCI.

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GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging

Biswas,

Maity,

Banik

et al. 2024

SN COMPUT. SCI.

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Application of Histopathology Image Analysis Using Deep Learning Networks

Hossain,

Armstrong,

Cook

et al. 2024

Hum-Cent Intell Syst

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As the rise in cancer cases, there is an increasing demand to develop accurate and rapid diagnostic tools for early intervention. Pathologists are looking to augment manual analysis with computer-based evaluation to develop more efficient cancer diagnostics reports. The processing of these reports from manual evaluation is time-consuming, where the pathologists focus on accurately segmenting individual cancer cells, a vital step in analysis. This paper describes the design and validation of an application which has been developed based on deep learning networks. The application includes a workflow of image pre-processing followed by synthetic image generation, which is crucial due to the lack of training data in pathology settings. The next steps are the segmentation of nuclei regions and overlapping nuclei splitting. An improved approach has been considered based on a cycle-consistent GAN network for synthetic image generation. The synthetic images were utilized in a modified U-net network. Accurately outlining the individual nucleus border assisted an automated system that split the nuclei cluster into the individual nucleus. The SSIM and PSNR values of synthetic images corresponding to original were 0.204 and 10.610. The DSC value of the network trained by the synthetic data was 0.984 which was higher than the network trained by original images (0.805). The developed application provided better individual nuclei segmentation performance, where the average accuracy of different group images was 0.97. This higher accuracy suggests the benefit of using synthetic images in a situation to avoid the scarcity of labeled histopathology images in deep networks.

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CGSNet: Cross-consistency guiding semi-supervised semantic segmentation network for remote sensing of plateau lake

Chen,

Shi,

Zhang

et al. 2024

Journal of Network and Computer Applications

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Semi-supervised medical image segmentation based on GAN with the pyramid attention mechanism and transfer learning

Cited by 3 publications

References 33 publications

GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging

GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging

Application of Histopathology Image Analysis Using Deep Learning Networks

CGSNet: Cross-consistency guiding semi-supervised semantic segmentation network for remote sensing of plateau lake

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