  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.
