Privacy-Preserving Multi-Modality-Based Computer-Aided Diagnosis Processing of Liver Diseases

Abstract

The recent advancement in the research fields of medical imaging and machine learning technologies greatly im- prove the accuracy of the content-based medical image retrie- val(C bmir ). Existing C bmir methods, however, often overlooks the effect of image details on retrieval accuracy, which is usually unsatisfactory. The C bmir of high-definition CT image (CI) based on deep learning technology, combined with patient symptom information, can precisely assist physicians in disease diagnosis and treatment. To address this issue, we take liver CI as an example in this article, and propose a privacy-preserving C omputer- A ided D iagnosis( Cad ) method of the liver diseases based on the multi-modal clinical information (e.g., patients’ CIs and symptoms, etc). Four enabling techniques such as a Wssln model, a unified similarity measure of CIs, a DM-T ree index and a lightweight privacy-preserving(LPP) scheme are pre- sented to facilitate the Cad processing of the large liver CIs. Ex- tensive experiments demonstrate that our proposed Cad me- thod outperform the state-of-the-art schemes by a large margin in terms of the retrieval accuracy and efficiency, respectively.