1. Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
- Author
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Lee, Sang-gil, Kim, Eunji, Bae, Jae Seok, Kim, Jung Hoon, and Yoon, Sungroh
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model's performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system., Comment: IEEE TETCI. 14 pages, 8 figures, 5 tables
- Published
- 2021
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