# Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications

## TL;DR

This paper introduces Parallel Medical Imaging (PMI), a novel framework that integrates data-driven learning and medical domain knowledge to improve diagnostic accuracy and interpretability in medical image analysis.

## Contribution

The paper proposes a new PMI framework that combines interactive ACP-based parallel intelligence with artificial imaging systems for enhanced medical diagnosis.

## Key findings

- Effective in mammogram and skin lesion analysis
- Improves generalization and interpretability of diagnostic models
- Demonstrated on multiple public datasets

## Abstract

There has been much progress in data-driven artificial intelligence technology for medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI.