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7 March 2022 Kidney cancer diagnosis using deep learning
Andrew Cheng, Michael Icaza, Nicholas Judd, Jason Smith, Sushmita Mukherjee, Manu Jain, Binlin Wu
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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.
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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
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KEYWORDS
Kidney
Cancer
Tumors
Image classification
Data modeling
Tumor growth modeling
Visual process modeling
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