Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medical objects, analyzing small areas in medical images remains challenging. This difficulty arises due to information losses and compression defects from convolution and pooling operations in CNNs, which become more pronounced as the network deepens, especially for small medical objects. To address these challenges, we propose a novel scale-variant attention-based network (SvANet) for accurately segmenting small-scale objects in medical images. The SvANet consists of scale-variant attention, cross-scale guidance, Monte Carlo 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疾病转化的风险，从而提高有效治疗的概率。识别具有微小病理区域的轻度症状可作为不良预警，是疾病早期诊断的基础。尽管深度学习算法，特别是卷积神经网络（CNN），在医学目标分割中展现出潜力，但分析医学图像中的微小区域仍具挑战性。这一困难源于CNN中卷积和池化操作造成的信息损失与压缩缺陷，随着网络加深，这些缺陷对于小型医学目标尤为显著。为应对这些挑战，我们提出了一种新颖的基于尺度变异注意力的网络（SvANet），用于精确分割医学图像中的小尺度目标。SvANet由尺度变异注意力、跨尺度引导、蒙特卡洛注意力以及视觉Transformer构成，该网络整合了跨尺度特征并缓解了压缩伪影，从而增强了对小型医学目标的判别能力。定量实验结果表明SvANet具有优越性能，在KiTS23、ISIC 2018、ATLAS、PolypGen、TissueNet、FIVES和SpermHealth数据集中，对图像面积占比小于1%的肾脏肿瘤、皮肤病变、肝脏肿瘤、息肉、手术切除细胞、视网膜血管及精子进行分割时，其平均Dice系数分别达到96.12%、96.11%、89.79%、84.15%、80.25%、73.05%和72.58%。