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A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI

Autor: Wang, Peng, Wu, Yuhsuan, Lai, Bolin, Zhou, Xiao-Yun, Lu, Le, Liu, Wendi, Zhou, Huabang, Huang, Lingyun, Xiao, Jing, Harrison, Adam P., Jia, Ningyang, Hu, Heping

Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with eithe

Externí odkaz: http://arxiv.org/abs/2110.08817

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Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data

Autor: Lai, Bolin, Wu, Yuhsuan, Zhou, Xiao-Yun, Wang, Peng, Lu, Le, Huang, Lingyun, Han, Mei, Xiao, Jing, Hu, Heping, Harrison, Adam P.

Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the high costs of annotations hinder the collection of large and completely

Externí odkaz: http://arxiv.org/abs/2103.12972

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Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach

Autor: Lai, Bolin, Wu, Yuhsuan, Bai, Xiaoyu, Zhou, Xiao-Yun, Wang, Peng, Cai, Jinzheng, Huo, Yuankai, Huang, Lingyun, Xia, Yong, Xiao, Jing, Lu, Le, Hu, Heping, Harrison, Adam

Using radiological scans to identify liver tumors is crucial for proper patient treatment. This is highly challenging, as top radiologists only achieve F1 scores of roughly 80% (hepatocellular carcinoma (HCC) vs. others) with only moderate inter-rate

Externí odkaz: http://arxiv.org/abs/2012.06964

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