The development of medical imaging techniques has made a significant
contribution to clinical decision-making. However, the existence of suboptimal
imaging quality, as indicated by irregular illumination or imbalanced
intensity, presents significant obstacles in automating disease screening,
analysis, and diagnosis. Existing approaches for natural image enhancement are
mostly trained with numerous paired images, presenting challenges in data
collection and training costs, all while lacking the ability to generalize
effectively. Here, we introduce a pioneering training-free Diffusion Model for
Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its
unsupervised enhancement capabilities across various medical image modalities
without the need for any fine-tuning. It accomplishes this by relying solely on
a single pre-trained model from ImageNet. We conduct a comprehensive evaluation
on 13 imaging modalities and over 15 medical types, demonstrating better
qualities, robustness, and accuracy than other modality-specific and
data-inefficient models. By delivering high-quality enhancement and
corresponding accuracy downstream tasks across a wide range of tasks, UniMIE
exhibits considerable potential to accelerate the advancement of diagnostic
tools and customized treatment plans.

无需训练的 UniMIE 扩散模型通过依赖单个预训练的 ImageNet 模型，在各种医学图像模态中展示了无监督增强能力，具有更好的质量、鲁棒性和准确性，有助于加速诊断工具和定制化治疗计划的发展。

医学图像的亮度调整

Lightening Anything in Medical Images

Brain disorders are a major challenge to global health, causing millions of
deaths each year. Accurate diagnosis of these diseases relies heavily on
advanced medical imaging techniques such as Magnetic Resonance Imaging (MRI)
and Computed Tomography (CT). However, the scarcity of annotated data poses a
significant challenge in deploying machine learning models for medical
diagnosis. To address this limitation, deep learning techniques have shown
considerable promise. Domain adaptation techniques enhance a model's ability to
generalize across imaging modalities by transferring knowledge from one domain
(e.g., CT images) to another (e.g., MRI images). Such cross-modality adaptation
is essential to improve the ability of models to consistently generalize across
different imaging modalities. This study collected relevant resources from the
Kaggle website and employed the Maximum Mean Difference (MMD) method - a
popular domain adaptation method - to reduce the differences between imaging
domains. By combining MMD with Convolutional Neural Networks (CNNs), the
accuracy and utility of the model is obviously enhanced. The excellent
experimental results highlight the great potential of data-driven domain
adaptation techniques to improve diagnostic accuracy and efficiency, especially
in resource-limited environments. By bridging the gap between different imaging
modalities, the study aims to provide clinicians with more reliable diagnostic
tools.

大脑疾病的精确诊断依赖于先进的医学成像技术，然而缺乏标注数据对于机器学习模型的部署造成了挑战。这项研究采用了深度学习和领域适应技术，结合了最大均值差异方法和卷积神经网络，改善了医学图像模态间的泛化能力，并通过提高诊断准确性和效率，为临床医生提供了更可靠的诊断工具。