arXiv:2605.26287v1 Announce Type: new 
Abstract: Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs may overlook critical areas in medical images, where even subtle changes can indicate disease. To address this limitation, we propose a novel approach that utilizes a multifractal measure (Renyi entropy) to optimize the masking strategy. Our method, termed Multifractal-Optimized Masked Autoencoder (MO-MAE), employs a multifractal analysis to identify regions of high complexity and information content. By focusing the masking process on these areas, MO-MAE ensures that the model learns to reconstruct the most diagnostically relevant features. This approach is particularly beneficial for medical imaging, where fine-grained inspection of tissue structures is crucial for accurate diagnosis. We evaluate MO-MAE on several medical datasets covering various diseases, including MedMNIST and COVID-CT. Our results demonstrate that MO-MAE achieves promising performance, surpassing other basiline and state-of-the-art models. The proposed method also adds minimum computational overhead as the computation of the proposed measure is straightforward. Our findings suggest that the multifractal-optimized masking strategy enhances the model's ability to capture and reconstruct complex tissue structures, leading to more accurate and efficient medical image representation. The proposed MO-MAE framework offers a promising direction for improving the accuracy and efficiency of deep learning models in medical image analysis, potentially advancing the field of computer-aided diagnosis.

تم اقتراح نهج جديد يسمى مشفر تلقائي مقنع مُحسَّن متعدد الفراكتلات (MO-MAE) لتحسين تصنيف الصور الطبية من خلال تحسين استراتيجية التعتيم باستخدام تحليل متعدد الفراكتلات. تركز هذه الطريقة على المناطق ذات التعقيد العالي في الصور الطبية، مما يضمن أن الميزات التشخيصية الحرجة تُعطى الأولوية خلال عملية إعادة البناء.

Se ha propuesto un enfoque novedoso llamado Autoencoder Máscara Optimizado por Multifractales (MO-MAE) para mejorar la clasificación de imágenes médicas mediante la optimización de la estrategia de enmascaramiento utilizando análisis multifractal. Este método se centra en regiones de alta complejidad en las imágenes médicas, asegurando que las características diagnósticas críticas se prioricen durante el proceso de reconstrucción.

Une nouvelle approche appelée l'autoencodeur masqué optimisé par multifractales (MO-MAE) a été proposée pour améliorer la classification des images médicales en optimisant la stratégie de masquage à l'aide d'une analyse multifractale. Cette méthode se concentre sur les régions de haute complexité dans les images médicales, garantissant que les caractéristiques diagnostiques critiques sont prioritaires lors du processus de reconstruction.

A novel approach called the Multifractal-Optimized Masked Autoencoder (MO-MAE) has been proposed to enhance medical image classification by optimizing the masking strategy using multifractal analysis. This method focuses on regions of high complexity in medical images, ensuring that critical diagnostic features are prioritized during the reconstruction process.