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Two-Stage Segmentation of Lung Cancer Metastasis Lesions by Fusion of Multi-Resolution Features
School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
Shanghai Pulmonary Hospital, Shanghai, China
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- 1 School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
- 2 School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
- 3 Shanghai Pulmonary Hospital, Shanghai, China
- 4 Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
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Abstract
The deep learning method automatically extracts advanced features from a large amount of data, avoiding cumbersome manual feature screening, and using digital pathology and artificial intelligence technology to build a computer-aided diagnosis system to help pathologists quickly make objective and reliable diagnoses and improve work efficiency. Because pathological images are limited by factors such as sample size, manual labeling expertise, and complexity, artificial intelligence algorithms have not been extensively and in-depth researched on pathological images of lung cancer metastasis. Therefore, this paper proposes a lung cancer metastasis segmentation method based on pathological images, to further improve the computer-aided diagnosis method of lung cancer.
KeywordsTransfer LearningPathological ImageACR-UNetDeep LearningCancer Metastasis
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