Pooling strategy for full-reference IQA via general means

Guojun, 刘国军 Liu; Li-xia, 高丽霞 Gao; Li-qi, 陈丽奇 Chen

doi:10.3788/ope.20172503.0742

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Convolutional Block Attraction Module1

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“…In principle, SAM pools the average and the maximum in the channel dimension by compress channels [8] . The formula is as follows [9] :…”

Section: Convolutional Block Attraction Modulementioning

confidence: 99%

Detection and recongnition of pulmonary nodules based on convolution neural network

Shi,

Shu

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Lung cancer is the disease with the highest incidence rate and mortality of cancer in China, which seriously threatens human life safety. Pulmonary nodules are the main factor leading to lung cancer, and their precise identification plays a crucial role in clinical diagnosis. This paper proposes a lung nodule detection model that combines global image information to address issues. The model is based on improved YOLOV5 network. Finally, comparative experiments have verified the accuracy and effectiveness of this model.

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“…In principle, SAM pools the average and the maximum in the channel dimension by compress channels [8] . The formula is as follows [9] :…”

Section: Convolutional Block Attraction Modulementioning

confidence: 99%