Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic

Yutong Zhong; Yan Piao

doi:10.1117/12.3009593

16 October 2023 Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic

Yutong Zhong, Yan Piao

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031Q (2023) https://doi.org/10.1117/12.3009593
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Mammography is main tests used for breast cancer risk assessment. However, the mass segmentation and classification of mammograms are extremely challenging. To reduce computational costs and the workloads of radiologists, deep neural networks have been widely used in various medical image segmentation and classification tasks. Since there are some common features between these two tasks, a multi-task learning approach to solve both tasks are a promising direction. We propose a mix Transformer depth-wise separable convolution U-Network (MTDUNet) for mass segmentation of mammograms. We introduce depth-wise separable convolutions to replace traditional convolutions and improve the network’s perception of multi-scale features within the receptive field. Additionally, due to the inherent limitations of convolutional networks, we introduce mix transformer to model remote contextual information. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the MTDUNet results and INBreast and CBIS-DDSM data were 89.90% and 83.63%, respectively. The experimental results indicate that MTDUNet can significantly reduce the spatial complexity of medical image segmentation networks and effectively save computational resources.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yutong Zhong and Yan Piao "Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031Q (16 October 2023); https://doi.org/10.1117/12.3009593

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KEYWORDS

Image segmentation

Convolution

Mammography

Transformers

RGB color model

Medical imaging

Breast

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