Abstract
The classification of non-small cell lung cancer (NSCLC) is a challenging task that faces two main problems that limit its performance. The first is that the feature regions used to discriminate NSCLC types are usually scattered, requiring classification models with the ability to accurately locate these feature regions and capture contextual information. Secondly, there are a large number of redundant regions in NSCLC pathology images that interfere with classification, but existing classification methods make it difficult to deal with them effectively. To solve these problems, we propose a fine-grained classification network, Progressive Jigsaw and Graph Convolutional Network (PJGC-Net). The network consists of two modules. For the first problem, we designed the GCN-Based multi-scale puzzle generation (GMPG) module to achieve fine-grained learning by training separate networks for different scale images, which can help the network identify and localize feature regions used to discriminate NSCLC types as well as their contextual information. For the second problem, we propose the jigsaw supervised progressive training (JSPT) module, which removes a large number of redundant regions and helps the proposed model focus on the effective discriminative regions. Our experimental results and visualization results on the LC25000 dataset demonstrate the feasibility of this method, and our method consistently outperforms other existing classification methods.
This work was supported by the National Natural Science Foundation of China (No. 62062057).
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Cao, Z., Jia, W. (2024). Fine-Grain Classification Method of Non-small Cell Lung Cancer Based on Progressive Jigsaw and Graph Convolutional Network. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_33
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DOI: https://doi.org/10.1007/978-981-99-8546-3_33
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