A multifractal-based masked auto-encoder: an application to medical images

• URL: http://arxiv.org/abs/2605.26287v1
• Date: Mon, 25 May 2026 19:20:31 GMT
• Title: A multifractal-based masked auto-encoder: an application to medical images
• Abstract summary: Masked autoencoders (MAE) have shown great promise in medical image classification.<n>We propose a novel approach that utilizes a multifractal measure (Renyi entropy) to optimize the masking strategy.<n>Our method, termed Multifractal-optimized Masked Autoencoder (MO-MAE), employs a multifractal analysis to identify regions of high complexity and information content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models [83.50100003741628]
  Vision and language models (VLMs) hold immense promise to transform biomedical imaging.<n>We introduce the Massive Multimodal Biomedical Understanding (MMBU) benchmark.<n>It is the largest vision and language benchmark to date, covering 35 submodalities with rich structured metadata.
  arXiv  Detail & Related papers  (2026-06-04T20:24:47Z)
• Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks [54.00822479127598]
  We introduce a medical vision-language task named Medical Diagnosis (MDS)<n>MDS aims to understand clinical queries for medical images and generate the corresponding segmentation masks as well as diagnostic results.<n>We propose Sim4Seg, a novel framework that improves the performance of diagnosis segmentation.
  arXiv  Detail & Related papers  (2025-11-10T03:22:42Z)
• Post-Processing Methods for Improving Accuracy in MRI Inpainting [4.574257127551285]
  Inpainting techniques aim to locally synthesize healthy brain tissues in tumor regions.<n>In this work, we evaluate state-of-the-art inpainting models and observe a saturation in their standalone performance.<n>We introduce a methodology combining model ensembling with efficient post-processing strategies such as median filtering, histogram matching, and pixel averaging.
  arXiv  Detail & Related papers  (2025-10-17T03:42:23Z)
• Lesion-Aware Post-Training of Latent Diffusion Models for Synthesizing Diffusion MRI from CT Perfusion [12.777937532267105]
  Latent diffusion models (LDMs) leverage efficient learning in compressed latent space.<n>This efficiency comes with a trade-off, potentially compromising crucial pixel-level detail.<n>We propose a novel post-training framework for LDMs in medical image-to-image translation by incorporating lesion-aware medical pixel space objectives.
  arXiv  Detail & Related papers  (2025-10-10T06:51:27Z)
• Mask What Matters: Controllable Text-Guided Masking for Self-Supervised Medical Image Analysis [2.6554246520306624]
  Mask What Matters is a controllable text-guided masking framework for self-supervised medical image analysis.<n>It consistently outperforms existing MIM methods, achieving gains of up to +3.1 percentage points in classification accuracy.<n>It achieves these improvements with substantially lower overall masking ratios.
  arXiv  Detail & Related papers  (2025-09-27T02:26:56Z)
• RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining [64.66825253356869]
  We propose a novel methodology that leverages dense radiology reports to define image-wise similarity ordering at multiple granularities.<n>We construct two comprehensive medical imaging retrieval datasets: MIMIC-IR for Chest X-rays and CTRATE-IR for CT scans.<n>We develop two retrieval systems, RadIR-CXR and model-ChestCT, which demonstrate superior performance in traditional image-image and image-report retrieval tasks.
  arXiv  Detail & Related papers  (2025-03-06T17:43:03Z)
• MRGen: Segmentation Data Engine for Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically important imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize data, for training segmentation models for underrepresented modalities.<n>We present MRGen, a data engine for controllable medical image synthesis conditioned on text prompts and segmentation masks.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
  We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation. We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks. Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
  arXiv  Detail & Related papers  (2024-03-09T13:37:02Z)
• MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder [26.830574964308962]
  We introduce MedFLIP, a Fast Language-Image Pre-training method for Medical analysis. We explore MAEs for zero-shot learning with crossed domains, which enhances the model's ability to learn from limited data. Lastly, we validate using language will improve the zero-shot performance for the medical image analysis.
  arXiv  Detail & Related papers  (2024-03-07T16:11:43Z)
• End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks [3.1133049660590615]
  We present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE) This approach yields an improved posterior distribution approximation compared to traditional Variational Autoencoders (VAE) Our method outperforms generative adversarial conditions, showcasing enhancements in image quality synthesis.
  arXiv  Detail & Related papers  (2023-11-17T11:56:53Z)
• AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation [67.97926983664676]
  Self-supervised masked image modeling has shown promising results on natural images. However, directly applying such methods to medical images remains challenging. We propose a novel self-supervised medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP)
  arXiv  Detail & Related papers  (2023-09-08T13:18:10Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.