Semi-supervised multiple image transformation network for metastatic lymph node diagnosis from CT

Weiping He; Wenxin Hu; Changquan Lu

doi:10.1117/12.2644675

12 October 2022 Semi-supervised multiple image transformation network for metastatic lymph node diagnosis from CT

Weiping He, Wenxin Hu, Changquan Lu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234226 (2022) https://doi.org/10.1117/12.2644675
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Effective treatment of lung cancer requires accurate diagnosis of mediastinal lymph node metastasis (LNM). In the current clinical practices, invasive examination is considered the gold standard, but it is inefficient and probably causes complications to the patient. Therefore, the automatic diagnosis of LNM from computed tomography (CT) images based on Deep Learning (DL) methods has become important research in aided diagnosis. DL methods require a large number of high-quality data to achieve good results. However, obtaining labels for LNM is difficult, the lack of annotations for LNM limits the accuracy of deep learning network classification. In this paper, we propose a semi-supervised multiple image transformation network (MITNet) for LNM prediction in CT images. We perform multiple image transformations on the images and input them to the feature extractors to extract multi-dimensional features, then use an attention-based module (ABM) to adaptively fuse the features to accurately predict LNM. In addition, in order to solve the problem of insufficient data volume, we introduce a semi-supervised learning strategy to train the network with CT image containing only lymph node (LN) segmentation annotations to improve its generalization ability. Experimental results show that our proposed method has an accuracy of 92.45% and outperforms several state-of-the-art methods.

Citation Download Citation

Weiping He, Wenxin Hu, and Changquan Lu "Semi-supervised multiple image transformation network for metastatic lymph node diagnosis from CT", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234226 (12 October 2022); https://doi.org/10.1117/12.2644675

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KEYWORDS

Computed tomography

Feature extraction

Lymphatic system

Data modeling

Mining

Image segmentation

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