Abstract
Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.
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Data Availability
The datasets of Mayo LDCT and LIDC-IDRI are publicly available at https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=52758026 and https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI, respectively.
Code Availability
Code is available at https://github.com/ymLeiFDU/Strided_Noise2Neighbors.
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Funding
This work was supported in part by National Natural Science Foundation of China (No. 62101136), Shanghai Municipal of Science and Technology Project (No. 20JC1419500), Shanghai Sailing Program (No. 21YF1402800), the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), ZJLab, Shanghai Center for Brain Science and Brain-Inspired Technology, the National Key R&D Program of China (No. 2018YFB1305104), and the Natural Science Foundation of Shanghai (No. 21ZR1403600).
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(I) Conception and design: All authors; (II) Collection of data: YL, HS; (III) Analysis of results: All authors; (IV) Manuscript writing and approval: All authors.
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Lei, Y., Zhang, J. & Shan, H. Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification. Phenomics 1, 257–268 (2021). https://doi.org/10.1007/s43657-021-00025-y
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DOI: https://doi.org/10.1007/s43657-021-00025-y