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Review of deep learning based methods for detecting focal liver lesions
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Wenyi DONG1, Weidong YANG1, Binghui TANG1, Qi WANG2, Hongyu XIAO3
Journal of Graphics | 2026, 47(1) : 1 - 16
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Journal of Graphics | 2026, 47(1): 1-16
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Review of deep learning based methods for detecting focal liver lesions
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Wenyi DONG1, Weidong YANG1, Binghui TANG1, Qi WANG2, Hongyu XIAO3
Affiliations
  • 1 School of Mechanical Engineering, Hebei University of Technology, Tianjin 300103, China
  • 2 Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei 050011, China
  • 3 Chinese People’s Liberation Army Aviation School, Beijing 101123, China
Published: 2026-02-28 doi: 10.11996/JG.j.2095-302X.2026010001
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The detection of Focal Liver Lesions (FLLs) is crucial for disease diagnosis and treatment. Traditional detection methods face many challenges, and the application of deep-learning technology brings new opportunities. In view of this, this paper systematically reviewed the deep-learning-based FLLs detection methods, and provided specific research directions for the development of FLLs detection technology by analyzing the advantages and disadvantages of related technologies. First, the public datasets of liver radiological images were organized and summarized, and the key role of data preprocessing in improving model performance was expounded. Secondly, the 2D and 3D detection algorithms based on convolutional neural networks, Transformer, knowledge distillation, and other technologies were compared and analyzed, revealing the technical evolution path from local feature modeling to global spatio-temporal correlation. In addition, the temporal feature fusion methods for multi-phase images were examined in depth, providing new ideas for dynamic lesion characterization. The review showed that existing methods had achieved breakthroughs in detection accuracy and efficiency, but still faced challenges such as insufficient sensitivity to small lesions, weak cross-device generalization, and lack of clinical verification. Future research was recommended to accelerate the clinical transformation and application of deep learning in auxiliary diagnosis of liver lesions through multi-center data collaboration, lightweight algorithm design, and enhanced interpretability.

deep learning  /  focal liver lesions  /  object detection  /  computerized tomography scan  /  multi-phase
Wenyi DONG, Weidong YANG, Binghui TANG, Qi WANG, Hongyu XIAO. Review of deep learning based methods for detecting focal liver lesions[J]. Journal of Graphics, 2026 , 47 (1) : 1 -16 . DOI: 10.11996/JG.j.2095-302X.2026010001
  • Government funded Clinical Medicine Excellent Talent Training Project in 2022(Ji Cai Yu Fu [2022]180)
Year 2026 volume 47 Issue 1
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Article Info
doi: 10.11996/JG.j.2095-302X.2026010001
  • Receive Date:2025-03-19
  • Online Date:2026-05-19
  • Published:2026-02-28
Article Data
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History
  • Received:2025-03-19
  • Accepted:2025-06-18
Funding
Government funded Clinical Medicine Excellent Talent Training Project in 2022(Ji Cai Yu Fu [2022]180)
Affiliations
    1 School of Mechanical Engineering, Hebei University of Technology, Tianjin 300103, China
    2 Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei 050011, China
    3 Chinese People’s Liberation Army Aviation School, Beijing 101123, China

Corresponding:

YANG Weidong,E-mail:
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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