Transformers-based Ensemble Methods for Medical Imaging: A Theoretical and Experimental Study
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Date
2024
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Faculty of Sciences
Abstract
Medical image classification accuracy significantly aids doctors in diagnosing diseases and planning treatments. Although Transformers, a cutting-edge technology in artificial intelligence, perform exceptionally well in various image classification tasks, their ability to capture the subtle details within medical images can be limited. To address this challenge, we propose, in this thesis, a novel ensemble method that leverages Transformers to improve medical image classification. We employ eight diverse medical imaging datasets and implement eight different Transformer-based ensemble methods on each dataset, covering several medical imaging areas such as magnetic resonance, computed tomography, magnetic resonance, dermatoscopic, chest X-ray, screening Mammography of medical imaging classification.
A deep experimental study of the proposed approach is conducted to evaluate its effectiveness in the medical image classification domain. Furthermore, an ablation study is performed to identify the optimal combination of base models for each ensemble method across different datasets. Our experiments encompass datasets of varying sizes, acknowledging the ongoing challenges of limited data availability in medical imaging. Despite this limitation, our ensemble method approach consistently outperforms state-of the-art methods across multiple datasets. This demonstrates its effectiveness in alleviating limitations associated with data size and diversity.
These findings highlight the potential of Transformers-based ensemble methods to revolutionize medical image classification.
This paves the way for improved diagnostic accuracy and treatment decision-making in clinical settings.