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Medical AI for Early Detection of Lung Cancer: A Survey

Autor: Cai, Guohui, Cai, Ying, Zhang, Zeyu, Cao, Yuanzhouhan, Wu, Lin, Ergu, Daji, Liao, Zhinbin, Zhao, Yang

Lung cancer remains one of the leading causes of morbidity and mortality worldwide, making early diagnosis critical for improving therapeutic outcomes and patient prognosis. Computer-aided diagnosis (CAD) systems, which analyze CT images, have proven

Externí odkaz: http://arxiv.org/abs/2410.14769

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Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching

Autor: Niu, Wenqi, Wang, Yingchao, Cai, Guohui, Hou, Hanpo

Knowledge Distillation (KD) has emerged as a pivotal technique for neural network compression and performance enhancement. Most KD methods aim to transfer dark knowledge from a cumbersome teacher model to a lightweight student model based on Kullback

Externí odkaz: http://arxiv.org/abs/2410.06561

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MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

Autor: Cai, Guohui, Cai, Ying, Zhang, Zeyu, Ergu, Daji, Cao, Yuanzhouhan, Hu, Binbin, Liao, Zhibin, Zhao, Yang

Pulmonary nodules are critical indicators for the early diagnosis of lung cancer, making their detection essential for timely treatment. However, traditional CT imaging methods suffered from cumbersome procedures, low detection rates, and poor locali

Externí odkaz: http://arxiv.org/abs/2409.14028

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