Abstract
Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks, particularly in image generation. However, their notable performance heavily relies on labelled datasets, which limits their application in medical images due to the associated high-cost annotations. Current DPM-related methods for lesion detection in medical imaging, which can be categorized into two distinct approaches, primarily rely on image-level annotations. The first approach, based on anomaly detection, involves learning reference healthy brain representations and identifying anomalies based on the difference in inference results. In contrast, the second approach, resembling a segmentation task, employs only the original brain multi-modalities as prior information for generating pixel-level annotations. In this paper, our proposed model - discrepancy distribution medical diffusion (DDMD) - for lesion detection in brain MRI introduces a novel framework by incorporating distinctive discrepancy features, deviating from the conventional direct reliance on image-level annotations or the original brain modalities. In our method, the inconsistency in image-level annotations is translated into distribution discrepancies among heterogeneous samples while preserving information within homogeneous samples. This property retains pixel-wise uncertainty and facilitates an implicit ensemble of segmentation, ultimately enhancing the overall detection performance. Thorough experiments conducted on the BRATS2020 benchmark dataset containing multimodal MRI scans for brain tumour detection demonstrate the great performance of our approach in comparison to state-of-the-art methods.
Abstract (translated)
扩散概率模型（DPMs）在计算机视觉任务中表现出了显著的有效性，特别是在图像生成方面。然而，它们显著的性能很大程度上依赖于带有标签的数据集，这限制了它们在医学图像中的应用，因为相关的高成本标注。目前，与DPM相关的医学影像病变检测方法，可以分为两种不同的方法，主要依赖于图像级的标注。基于异常检测的方法包括学习参考健康的大脑表示并基于推理结果差异识别异常。相反，类似于分割任务的方法仅使用原始的大脑多模态作为生成像素级标注的前提信息。在本文中，我们提出的模型——差异分布医学扩散（DDMD）用于脑部MRI的病变检测，引入了一种新颖的框架，通过包含独特的差异特征，与传统直接依赖图像级标注或原始大脑模态的方法相比，有所区别。在我们的方法中，图像级注释的不一致性转化为异质样本之间的分布不一致性，同时保留信息在同一样本内。这种性质保留了像素级的不确定性，并促进了隐式集成的 segmentation，最终提高了整体检测性能。在包含多模态MRI扫描的大脑肿瘤检测基准数据集BRATS2020上进行的详细实验证明，与最先进的方法相比，我们的方法具有出色的性能。
URL
https://arxiv.org/abs/2405.04974
