Intelligent cell images segmentation system: based on SDN and moving transformer DOI Creative Commons
Jia Wu, Pan Yao, Qing Ye

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 22, 2024

Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially regions with a scarcity qualified healthcare professionals. This study aims to develop an intelligent system enhance diagnostic accuracy cytopathology images by addressing image noise and segmentation issues, thereby improving efficiency medical professionals disease diagnosis. We introduced innovative combining self-supervised algorithm, SDN, for denoising enhancement using UPerMVit model. The model's novel attention mechanisms modular architecture provide higher lower computational complexity than traditional methods. proposed effectively reduces accurately segments annotated highlighting cellular structures staff. enhances aids accurate pathological cells. Our offers reliable tool professionals, cytopathologic analysis. It provides significant technical support lacking adequate expertise.

Language: Английский

TransRNetFuse: a highly accurate and precise boundary FCN-transformer feature integration for medical image segmentation DOI Creative Commons

Baotian Li,

Jing Zhou,

Fangfang Gou

et al.

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(5)

Published: March 17, 2025

Language: Английский

Citations

0
Enhanced boundary-directed lightweight approach for digital pathological image analysis in critical oncological diagnostics DOI

Ou Luo,

Jing Zhou,

Fangfang Gou

et al.

Journal of X-Ray Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Background Pathological images play a crucial role in the diagnosis of critically ill cancer patients. Since patients often seek medical assistance when their condition is severe, doctors face urgent challenge completing accurate diagnoses and developing surgical plans within limited timeframe. The complexity diversity pathological require significant investment time from specialized physicians for processing analysis, which can lead to missing optimal treatment window. Purpose Current decision support systems are challenged by high computational deep learning models, demand extensive data training, making it difficult meet real-time needs emergency diagnostics. Method This study addresses issue malignant bone tumors such as osteosarcoma proposing Lightened Boundary-enhanced Digital Image Recognition Strategy (LB-DPRS). strategy optimizes self-attention mechanism Transformer model innovatively implements boundary segmentation enhancement strategy, thereby improving recognition accuracy tissue backgrounds nuclear boundaries. Additionally, this research introduces row-column attention methods sparsify matrix, reducing burden enhancing speed. Furthermore, proposed complementary further assists convolutional layers fully extracting detailed features . Results DSC value LB-DPRS reached 0.862, IOU 0.749, params was only 10.97 M. Conclusion Experimental results demonstrate that significantly improves efficiency while maintaining prediction interpretability, providing powerful efficient osteosarcoma.

Language: Английский

Citations

0
Pathological Image Segmentation Method Based on Multiscale and Dual Attention DOI Creative Commons
Jia Wu, Yuxia Niu, Ziqiang Ling

et al.

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Medical images play a significant part in biomedical diagnosis, but they have feature. The medical images, influenced by factors such as imaging equipment limitations, local volume effect, and others, inevitably exhibit issues like noise, blurred edges, inconsistent signal strength. These imperfections pose challenges create obstacles for doctors during their diagnostic processes. To address these issues, we present pathology image segmentation technique based on the multiscale dual attention mechanism (MSDAUnet), which consists of three primary components. Firstly, an denoising enhancement module is constructed using dynamic residual color histogram to remove noise improve clarity. Then, propose (DAM), extracts messages from both channel spatial dimensions, obtains key features, makes edge lesion area clearer. Finally, capturing information process addresses issue uneven strength certain extent. Each combined automatic pathological segmentation. Compared with traditional typical U‐Net model, MSDAUnet has better performance. On dataset provided Research Center Artificial Intelligence Monash University, IOU index high 72.7%, nearly 7% higher than that U‐Net, DSC 84.9%, also about U‐Net.

Language: Английский

Citations

2
Intelligent cell images segmentation system: based on SDN and moving transformer DOI Creative Commons
Jia Wu, Pan Yao, Qing Ye

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 22, 2024

Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially regions with a scarcity qualified healthcare professionals. This study aims to develop an intelligent system enhance diagnostic accuracy cytopathology images by addressing image noise and segmentation issues, thereby improving efficiency medical professionals disease diagnosis. We introduced innovative combining self-supervised algorithm, SDN, for denoising enhancement using UPerMVit model. The model's novel attention mechanisms modular architecture provide higher lower computational complexity than traditional methods. proposed effectively reduces accurately segments annotated highlighting cellular structures staff. enhances aids accurate pathological cells. Our offers reliable tool professionals, cytopathologic analysis. It provides significant technical support lacking adequate expertise.

Language: Английский

Citations

0