Kidney cancer diagnosis using deep learning

Andrew Cheng; Michael Icaza; Nicholas Judd; Jason Smith; Sushmita Mukherjee; Manu Jain; Binlin Wu

doi:10.1117/12.2610117

7 March 2022 Kidney cancer diagnosis using deep learning

Andrew Cheng, Michael Icaza, Nicholas Judd, Jason Smith, Sushmita Mukherjee, Manu Jain, Binlin Wu

Proceedings Volume PC11954, Optical Biopsy XX: Toward Real-Time Spectroscopic Imaging and Diagnosis; PC119540E (2022) https://doi.org/10.1117/12.2610117
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

In this study, we trained a convolutional neural network (CNN) utilizing a mix of recent CNN architectural design strategies. Our goals are to leverage these modern techniques to improve the binary classification of kidney tumor images obtained using Multi-Photon Microscopy (MPM). We demonstrate that incorporating these newer model design elements, coupled with transfer learning, image standardization, and data augmentation, leads to significantly increased classification performance over previous results. Our best model averages over 90% sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUROC) in image-level classification across cross-validation folds, superior to the previous best in all four metrics.

Conference Presentation

Citation Download Citation

Andrew Cheng, Michael Icaza, Nicholas Judd, Jason Smith, Sushmita Mukherjee, Manu Jain, and Binlin Wu "Kidney cancer diagnosis using deep learning", Proc. SPIE PC11954, Optical Biopsy XX: Toward Real-Time Spectroscopic Imaging and Diagnosis, PC119540E (7 March 2022); https://doi.org/10.1117/12.2610117

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available