Parallel architecture based on transformers and CNNs for renal cell carcinoma image segmentation

Bin Ao; Rong Liu; Qing Wen; Xin Wu; Kuan Li; Jianping Yin

doi:10.1117/12.2684507

1 August 2023 Parallel architecture based on transformers and CNNs for renal cell carcinoma image segmentation

Bin Ao, Rong Liu, Qing Wen, Xin Wu, Kuan Li, Jianping Yin

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127540M (2023) https://doi.org/10.1117/12.2684507
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Image segmentation of Renal Cell Carcinoma is a crucial prerequisite for pathologists to diagnose the disease and implement treatment. In various previous medical image segmentation tasks, convolutional neural networks(CNNs) have been widely used and have become a practical benchmark with significant success. However, the inherently local nature of the convolution operation leads to its inability to balance long-range relations and local dependencies in the modeling process, resulting in redundancy-deepening networks and the loss of local details. With its innate global self-attention mechanism, transformers were designed for sequence-to-sequence prediction to better extract global information. However, on account of the lack of low-level details, it may cause limited localization abilities. In this paper, we propose a parallel architecture based on Transformers and CNNs, combining Transformers and CNNs in a parallel manner to obtain global and local information by utilizing location-encoded Transformers model and local convolution neural networks, respectively. So that global contextual information can be captured more efficiently while maintaining a strong grasp of low-level spatial detailed information for Renal Cell Carcinoma image segmentation. Through a series of comparative experiments and extension experiments, the effectiveness and progressiveness of our method were validated, demonstrating remarkable potential in the image segmentation task of Renal Cell Carcinoma.

Citation Download Citation

Bin Ao, Rong Liu, Qing Wen, Xin Wu, Kuan Li, and Jianping Yin "Parallel architecture based on transformers and CNNs for renal cell carcinoma image segmentation", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127540M (1 August 2023); https://doi.org/10.1117/12.2684507

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KEYWORDS

Transformers

Image segmentation

Feature fusion

Visual process modeling

Education and training

Feature extraction

Convolutional neural networks

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