This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

翻译：本文介绍了一种新颖的单阶段端到端检测器，专门用于医学图像中小病灶的检测。由于小病灶的外观及其所处的多样化上下文背景，精确定位小病灶颇具挑战。为此，我们的方法提出了一种新型像素级锚点，该锚点能够动态地向目标病灶移动以进行检测。我们将这种新架构称为GravityNet，并将新型锚点称为重力点，因为它们似乎被病灶“吸引”。我们针对两个涉及小病灶的成熟医学问题进行了实验，以评估所提方法的性能：数字乳腺X线摄影中的微钙化检测和数字眼底图像中的微动脉瘤检测。我们的方法在这些医学成像任务中有效检测小病灶方面展现了令人满意的结果。