Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. A mild syndrome with small infected regions is an ominous warning and is foremost in the early diagnosis of diseases. Deep learning algorithms, such as convolutional neural networks (CNNs), have been used to segment natural or medical objects, showing promising results. However, analyzing medical objects of small areas in images remains a challenge due to information losses and compression defects caused by convolution and pooling operations in CNNs. These losses and defects become increasingly significant as the network deepens, particularly for small medical objects. To address these challenges, we propose a novel scale-variant attention-based network (SvANet) for accurate small-scale object segmentation in medical images. The SvANet consists of Monte Carlo attention, scale-variant attention, and vision transformer, which incorporates cross-scale features and alleviates compression artifacts for enhancing the discrimination of small medical objects. Quantitative experimental results demonstrate the superior performance of SvANet, achieving 96.12%, 96.11%, 89.79%, 84.15%, 80.25%, 73.05%, and 72.58% in mean Dice coefficient for segmenting kidney tumors, skin lesions, hepatic tumors, polyps, surgical excision cells, retinal vasculatures, and sperms, which occupy less than 1% of the image areas in KiTS23, ISIC 2018, ATLAS, PolypGen, TissueNet, FIVES, and SpermHealth datasets, respectively.

翻译：早期检测与准确诊断能够预测恶性病变风险，从而提高有效治疗概率。感染区域较小的轻微症状是重要的预警信号，在疾病早期诊断中至关重要。卷积神经网络（CNN）等深度学习算法已被用于自然或医学目标分割，展现出良好效果。然而，由于CNN中卷积和池化操作导致的信息损失与压缩缺陷，分析图像中的小区域医学目标仍具挑战性。随着网络深度增加，这些损失与缺陷对小尺度医学目标的影响尤为显著。为应对这些挑战，我们提出一种新颖的尺度可变注意力网络（SvANet），用于医学图像中的小尺度目标精确分割。SvANet由蒙特卡洛注意力、尺度可变注意力和视觉Transformer构成，通过融合跨尺度特征并缓解压缩伪影，增强了对小尺度医学目标的判别能力。定量实验结果表明SvANet具有卓越性能，在KiTS23、ISIC 2018、ATLAS、PolypGen、TissueNet、FIVES和SpermHealth数据集中，对图像占比不足1%的肾脏肿瘤、皮肤病灶、肝脏肿瘤、息肉、手术切除细胞、视网膜血管和精子的分割任务中，平均Dice系数分别达到96.12%、96.11%、89.79%、84.15%、80.25%、73.05%和72.58%。