Differentially private fine-tuned NF-Net to predict GI cancer type
Journal:
arXiv
Published Date:
Feb 17, 2025
Abstract
Based on global genomic status, the cancer tumor is classified as
Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is
used to diagnose MSI, whereas radiation and chemotherapy are used for MSS.
Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor
into MSI vs. MSS to provide appropriate treatment. The existing literature
showed that deep learning could directly predict the class of GI cancer tumors
from histological images. However, deep learning (DL) models are susceptible to
various threats, including membership inference attacks, model extraction
attacks, etc. These attacks render the use of DL models impractical in
real-world scenarios. To make the DL models useful and maintain privacy, we
integrate differential privacy (DP) with DL. In particular, this paper aims to
predict the state of GI cancer while preserving the privacy of sensitive data.
We fine-tuned the Normalizer Free Net (NF-Net) model. We obtained an accuracy
of 88.98\% without DP to predict (GI) cancer status. When we fine-tuned the
NF-Net using DP-AdamW and adaptive DP-AdamW, we got accuracies of 74.58% and
76.48%, respectively. Moreover, we investigate the Weighted Random Sampler
(WRS) and Class weighting (CW) to solve the data imbalance. We also evaluated
and analyzed the DP algorithms in different settings.
