[1] J X FENG, J JIANG. Deep learning-based chest CT image features in diagnosis of lung cancer. Computational and Mathematical Methods in Medicine, 2022, 4153211(2022).

[2] [2] 2张永平， 兰朋训， 周兆霞. MRI和骨显像诊断骨转移瘤的临床价值分析［J］. 医学影像学杂志， 2017， 27（10）： 2040-2042.ZHANGY P， LANP X， ZHOUZH X. Clinical value of MRI and bone scintigraphy in the diagnosis of bone metastases［J］. Journal of Medical Imaging， 2017， 27（10）： 2040-2042.（in Chinese）

[3] M T MADSEN. Recent advances in SPECT imaging. Journal of Nuclear Medicine： Official Publication, 48, 661-673(2007).

[4] M SADIK, I HAMADEH, P NORDBLOM et al. Computer-assisted interpretation of planar whole-body bone scans. J Nucl Med, 49, 1958-1965(2008).

[5] A ASLANTAS, E DANDIL, S SAǦLAM et al. CADBOSS： a computer-aided diagnosis system for whole-body bone scintigraphy scans. Journal of Cancer Research and Therapeutics, 12, 787-792(2016).

[6] M ANTONINA. Object-oriented classification approach for bone metastasis mapping from whole-body bone scintigraphy. Physica Medica, 84, 141-148(2021).

[7] F G ELFARRA, M A CALIN, S V PARASCA. Computer-aided detection of bone metastasis in bone scintigraphy images using parallelepiped classification method. Annals of Nuclear Medicine, 33, 866-874(2019).

[8] Z Y WANG, X T WEN, Y H LU et al. Exploiting machine learning for predicting skeletal-related events in cancer patients with bone metastases. Oncotarget, 7, 12612-12622(2016).

[9] L M LU, C L ZHANG, K CAO et al. A multichannel CNN-GRU model for human activity recognition. IEEE Access, 10, 66797-66810(2022).

[10] Z W ZHENG, L X LIU, X Y CHEN et al. Construction of Bisection Model of SPECT Bone Scan Image Based on VGGNet, 150-154(2021).

[11] N PAPANDRIANOS, E PAPAGEORGIOU, A ANAGNOSTIS et al. Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application. PLoS One, 15(2020).

[12] C NTAKOLIA, D E DIAMANTIS, N PAPANDRIANOS et al. A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine： a case study on prostate cancer patients. Healthcare, 8, 493(2020).

[13] Q LIN, C G CAO, T T LI et al. dSPIC： a deep SPECT image classification network for automated multi-disease， multi-lesion diagnosis. BMC Medical Imaging, 21, 122(2021).

[14] B ABDOLLAHI, N TOMITA, S HASSANPOUR. Data augmentation in training deep learning models for medical image analysis. Intelligent Systems Reference Library, 167-180(2020).

[15] KORA PADMAVATHI. Transfer learning techniques for medical image analysis： a review. Biocybernetics and Biomedical Engineering, 42, 79-107(2022).

[16] ZIA TEHSEEN. VANT-GAN： adversarial learning for discrepancy-based visual attribution in medical imaging. Pattern Recognition Letters, 156, 112-118(2022).

[17] C M WU, Z ZENG. A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing. PLoS One, 16(2021).

[18] O RONNEBERGER, P FISCHER, T BROX. U-net： convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science, 234-241(2015).

[19] Q B HOU, D Q ZHOU, J S FENG. Coordinate Attention for Efficient Mobile Network Design, 13708-13717(2021).

[20] J XU, J WANG, X XU et al. Image recognition for different developmental stages of rice by RAdam deep convolutional neural networks. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 37, 143-150(2021).

[21] A HOWARD, M SANDLER, B CHEN et al. Searching for MobileNetV3, 1314-1324.

[22] K LIU, S T YU, S D LIU. An improved InceptionV3 network for obscured ship classification in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4738-4747(2020).

[23] M TAN, Q V LE. EfficientNet： Rethinking Model Scaling for Convolution Neural Networks, 6105-6114(2019).