Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning

Xin Deng; Min Li; Xiaojun Ren; Xiuyan Zheng; Fei Liang; Liang He; Min Zhu

doi:10.1117/12.3005930

10 October 2023 Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning

Xin Deng, Min Li, Xiaojun Ren, Xiuyan Zheng, Fei Liang, Liang He, Min Zhu

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991Y (2023) https://doi.org/10.1117/12.3005930
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model’s performance.

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

Citation Download Citation

Xin Deng, Min Li, Xiaojun Ren, Xiuyan Zheng, Fei Liang, Liang He, and Min Zhu "Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991Y (10 October 2023); https://doi.org/10.1117/12.3005930

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KEYWORDS

Image classification

Colorectal cancer

Pathology

Feature extraction

Histopathology

Neural networks

Feature fusion

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