Keywords: Kidney, Machine Learning/Artificial Intelligence
Deep learning has been successful in predicting tumor malignancy. Clear cell renal carcinoma (ccRCC) diagnosis may help in decision making between active surveillance and definitive intervention. In this study, we investigate the effects of different factors on the performance of deep learning classification of ccRCC using T2w images. We demonstrate that the performance of 15 different CNN models varied substantially. The choice of CNN models, the cropped image size, and the type of inputs greatly altered the performance of ccRCC classification. We achieved the best AUC value of 0.81, which is close to the reported performance of radiologists.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords