RobustDeiT: Noise-Robust Vision Transformers for Medical Image Classification - Taassori, Mehdi (Image Analysis and Stereology, 2025)
Effective classification of medical images is vital for accurate diagnosis and treatment, but noisy datasets remain a significant challenge, obscuring critical features and leading to unreliable predictions. To address this, we propose RobustDeiT, a noise-robust architecture based on the Data-efficient Image Transformer (DeiT), tailored for medical image classification in noisy environments. By integrating a multi-stage preprocessing pipeline, our approach systematically reduces noise, enhances contrast, and highlights fine details, ensuring the preservation of essential features. Advanced denoising methods, contrast enhancement with Contrast Limited Adaptive Histogram Equalization, and sharpening via unsharp masking collectively improve image quality, enabling the model to extract meaningful patterns. Extensive evaluations demonstrate that RobustDeiT achieves superior performance across diverse metrics, establishing its effectiveness in handling noisy medical imaging datasets and paving the way for reliable and accurate classification in real-world scenarios.