  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.
