Differentially private fine-tuned NF-Net to predict GI cancer type

TL;DR

This paper develops a differentially private deep learning model based on NF-Net to classify GI cancer types from histological images, balancing accuracy with privacy preservation.

Contribution

It introduces a novel application of differential privacy to fine-tune NF-Net for GI cancer classification, addressing data privacy concerns in medical imaging.

Findings

Achieved 88.98% accuracy without DP

Attained 74.58% and 76.48% accuracy with DP-AdamW and adaptive DP-AdamW

Analyzed the impact of DP algorithms and data imbalance solutions

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.