ArticlePatterns (New York, N.Y.)2022
Machine vision-assisted identification of the lung adenocarcinoma category and high-risk tumor area based on CT images.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.Current oncology (Toronto, Ont.) · 2024Review
- Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.PloS one · 2024Article
- A boundary-guided transformer for measuring distance from rectal tumor to anal verge on magnetic resonance images.Patterns (New York, N.Y.) · 2023Article
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8 authors.
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Abstract
Computed tomography (CT) is a widely used medical imaging technique. It is important to determine the relationship between CT images and pathological examination results of lung adenocarcinoma to better support its diagnosis. In this study, a bilateral-branch network with a knowledge distillation procedure (KDBBN) was developed for the auxiliary diagnosis of lung adenocarcinoma. KDBBN can automatically identify adenocarcinoma categories and detect the lesion area that most likely contributes to the identification of specific types of adenocarcinoma based on lung CT images. In addition, a knowledge distillation process was established for the proposed framework to ensure that the developed models can be applied to different datasets. The results of our comprehensive computational study confirmed that our method provides a reliable basis for adenocarcinoma diagnosis supplementary to the pathological examination. Meanwhile, the high-risk area labeled by KDBBN highly coincides with the related lesion area labeled by doctors in clinical diagnosis.
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