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

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

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

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

Published: March 17, 2025

Language: Английский

Multi-layer feature fusion and coarse-to-fine label learning for semi-supervised lesion segmentation of lung cancer

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113451 - 113451

Published: April 1, 2025

Language: Английский