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.

翻译：扩散概率模型（DPMs）在计算机视觉任务中，尤其是图像生成方面，已展现出显著的有效性。然而，其卓越性能严重依赖有标签的数据集，这限制了其在医学图像中的应用，因为相关标注成本高昂。当前用于医学影像病变检测的 DPM 相关方法主要依赖图像级标注，可分为两种不同途径。第一种方法基于异常检测，通过学习参考健康脑部表征，并根据推理结果的差异来识别异常。相比之下，第二种方法类似于分割任务，仅将原始脑部多模态作为先验信息来生成像素级标注。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 病变检测，引入了一个新颖框架，通过整合独特的差异特征，脱离了传统上对图像级标注或原始脑部模态的直接依赖。在我们的方法中，图像级标注的不一致性被转化为异质样本间的分布差异，同时保留了同质样本内的信息。这种特性保留了像素级的不确定性，并促进了隐式的分割集成，从而最终提升了整体检测性能。在包含多模态 MRI 脑肿瘤检测扫描的 BRATS2020 基准数据集上进行的充分实验表明，与最先进方法相比，我们的方法表现出了优异的性能。