ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation

[1] Checcucci E(2020)Applications of neural networks in urology: a systematic review Curr Opin Urol 30 788-807

[2] De Cillis S(2017)Renal mass and localized renal cancer: AUA Guideline J Urol 198 520-529

[3] Granato S(2015)Radiomics: images are more than pictures they are data Radiology 278 563-577

[4] Chang P(2019)Abdominal multi-organ segmentation with organ-attention networks and statistical fusion Med Image Anal 55 88-102

[5] Afyouni AS(2021)A systematic review of the automatic kidney segmentation methods in abdominal images Biocybern Biomed Eng 41 1601-1628

[6] Okhunov ZJCOIU(2023)HCIU: Hybrid clustered inception-based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images Int J Imaging Syst Technol 33 2203-2217

[7] Campbell S(2022)Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research Artif Intell Rev 55 1409-1439

[8] Uzzo Robert G(2020)Characterization of solid renal neoplasms using MRI-based quantitative radiomics features Abdominal Radiol 45 2840-2850

[9] Allaf Mohamad E(2020)Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging Clin Cancer Res 26 1944-1952

[10] Bass Eric B(2022)A radiomic-based machine learning algorithm to reliably differentiate benign renal masses from renal cell carcinoma Eur Urol Focus 8 988-994

[1] Checcucci E(2020)Applications of neural networks in urology: a systematic review Curr Opin Urol 30 788-807

[2] De Cillis S(2017)Renal mass and localized renal cancer: AUA Guideline J Urol 198 520-529

[3] Granato S(2015)Radiomics: images are more than pictures they are data Radiology 278 563-577

[4] Chang P(2019)Abdominal multi-organ segmentation with organ-attention networks and statistical fusion Med Image Anal 55 88-102

[5] Afyouni AS(2021)A systematic review of the automatic kidney segmentation methods in abdominal images Biocybern Biomed Eng 41 1601-1628

[6] Okhunov ZJCOIU(2023)HCIU: Hybrid clustered inception-based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images Int J Imaging Syst Technol 33 2203-2217

[7] Campbell S(2022)Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research Artif Intell Rev 55 1409-1439

[8] Uzzo Robert G(2020)Characterization of solid renal neoplasms using MRI-based quantitative radiomics features Abdominal Radiol 45 2840-2850

[9] Allaf Mohamad E(2020)Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging Clin Cancer Res 26 1944-1952

[10] Bass Eric B(2022)A radiomic-based machine learning algorithm to reliably differentiate benign renal masses from renal cell carcinoma Eur Urol Focus 8 988-994