  Universal Lesion Detection (ULD) in computed tomography (CT) plays an
essential role in computer-aided diagnosis. Promising ULD results have been
reported by anchor-based detection designs, but they have inherent drawbacks
due to the use of anchors: i) Insufficient training targets and ii)
Difficulties in anchor design. Diffusion probability models (DPM) have
demonstrated outstanding capabilities in many vision tasks. Many DPM-based
approaches achieve great success in natural image object detection without
using anchors. But they are still ineffective for ULD due to the insufficient
training targets. In this paper, we propose a novel ULD method, DiffULD, which
utilizes DPM for lesion detection. To tackle the negative effect triggered by
insufficient targets, we introduce a novel center-aligned bounding box padding
strategy that provides additional high-quality training targets yet avoids
significant performance deterioration. DiffULD is inherently advanced in
locating lesions with diverse sizes and shapes since it can predict with
arbitrary boxes. Experiments on the benchmark dataset DeepLesion show the
superiority of DiffULD when compared to state-of-the-art ULD approaches.
