Privacy-preserving Machine Learning for Medical Image Classification
This addresses privacy issues for medical professionals and patients in automated disease detection systems, but appears incremental as it applies known privacy-preserving techniques to a specific medical domain.
The paper tackled the problem of privacy concerns in medical image classification for pneumonia detection using chest x-ray images, aiming to secure patient data and model predictions without revealing them in clear text.
With the rising use of Machine Learning (ML) and Deep Learning (DL) in various industries, the medical industry is also not far behind. A very simple yet extremely important use case of ML in this industry is for image classification. This is important for doctors to help them detect certain diseases timely, thereby acting as an aid to reduce chances of human judgement error. However, when using automated systems like these, there is a privacy concern as well. Attackers should not be able to get access to the medical records and images of the patients. It is also required that the model be secure, and that the data that is sent to the model and the predictions that are received both should not be revealed to the model in clear text. In this study, we aim to solve these problems in the context of a medical image classification problem of detection of pneumonia by examining chest x-ray images.