DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis

• URL: http://arxiv.org/abs/2410.19166v1
• Date: Thu, 24 Oct 2024 21:16:56 GMT
• Title: DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis
• Authors: Mahtab Ranjbar, Mehdi Mohebbi, Mahdi Cherakhloo, Bijan Vosoughi. Vahdat,
• Abstract summary: We introduce a novel lightweight breast cancer classification approach using Vision Transformers (ViTs) By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image. Our proposed model achieves an accuracy of 96.00% $pm$ 0.48% for binary classification and 87.85% $pm$ 0.93% for multiclass classification, which is comparable to state-of-the-art models.
• Score: 0.0
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• Abstract: In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopathological images presents challenges due to the need for extensive manual annotations of whole-slide images (WSIs), as these models require large amounts of data to work effectively, which is costly and time-consuming. Furthermore, the quadratic computational cost of Vision Transformers (ViTs) is particularly prohibitive for large, high-resolution histopathological images, especially on edge devices with limited computational resources. In this study, we introduce a novel lightweight breast cancer classification approach using transformers that operates effectively without large datasets. By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image, which reduces computational cost. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. Our proposed model achieves an accuracy of 96.00% $\pm$ 0.48% for binary classification and 87.85% $\pm$ 0.93% for multiclass classification, which is comparable to state-of-the-art models while significantly reducing computational costs. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets.

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