Vol. 8, Special Issue 1 (2019)

Ensemble learning leverages the strengths of multiple base classifiers to achieve superior predictive accuracy and generalization compared to individual models. In the context of medical imaging, where data heterogeneity and complexity pose significant challenges, ensemble methods offer promising solutions for improving diagnostic accuracy and reliability. This survey systematically explores the landscape of ensemble learning techniques employed in the medical imaging domain, highlighting their strengths, limitations, and potential applications.

The paper begins by elucidating the fundamental concepts of ensemble learning and its relevance to medical image analysis. Subsequently, it categorizes the diverse ensemble approaches, including bagging, boosting, stacking, and hybrid methods, while providing a detailed discussion of their underlying mechanisms. Special attention is given to ensemble strategies tailored for medical imaging data, such as bagging with random feature selection, boosting with specialized image features, and stacking with heterogeneous modalities.

The review further delves into case studies and benchmark datasets used to evaluate the performance of ensemble models, shedding light on their efficacy across different medical imaging modalities, such as X-ray, MRI, CT, and ultrasound. The challenges and open research directions in the application of ensemble learning to medical imaging data are systematically outlined, guiding future research endeavors.