Background and objective: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in terms of the initial random weights and in terms of the statistical noise in the training dataset. And the disadvantage caused by adaptability is that deep convolutional networks usually have poor robustness or generalization when the models are trained using the extremely limited amount of labeled data, especially in the biomedical imaging informatics field.

Methods: In this paper, we propose to develop and test a stacked generalization U-shape network (SG-UNet) based on the zoom strategy applying to biomedical image segmentation. SG-UNet is essentially a stacked generalization architecture consisting of multiple sub-modules, which takes multi-resolution images as input and uses hybrid features to segment regions of interest and detect diseases under the multi-supervision. The proposed new SG-UNet applies the zoom of multi-supervision to do optimization search in global feature space without pre-training. Besides, the zoom loss function can gradually enhance the focus training on a sparse set of hard samples.

Results: We evaluated the proposed algorithm in comparison with several popular U-shape ensemble network architectures across multi-modal biomedical image segmentation tasks to segment malignant rectal cancers, polyps and glands from the three imaging modalities of computed tomography (CT), digital colonoscopy and histopathology images. Applying the proposed algorithm improves 3.116%, 2.676%, 2.356% on Dice coefficients, and 3.044%, 2.420%, 1.928% on F2-score for the three imaging modality datasets, respectively. The comparison results using different amounts of rectal cancer CT data show that the proposed algorithm has a slower tendency of diminishing marginal efficiency. And glands segmentation study results also support the feasibility of yielding comparable performance with other state-of-the-art methods.

Conclusions: The proposed algorithm can be trained more efficiently by using the small image datasets without using additional techniques such as fine-tuning, and achieves higher accuracy with less computational complexity than other stacked ensemble networks for biomedical image segmentation.

背景与目的：深度神经网络模型可以学习数据中的复杂非线性关系，并且具有出色的灵活性和适应性。这种灵活性的缺点是，无论是在初始随机权重方面还是在训练数据集中的统计噪声方面，它们都对初始条件敏感。适应性带来的缺点是，当使用极其有限的标记数据（特别是在生物医学成像信息学领域）训练模型时，深度卷积网络通常具有较差的鲁棒性或泛化性。

方法：在本文中，我们提出了基于缩放策略的堆叠通用U型网络（SG-UNet）的开发和测试，并将其应用于生物医学图像分割。SG-UNet本质上是一个由多个子模块组成的堆叠泛化体系结构，该体系结构将多分辨率图像作为输入，并使用混合特征来分割感兴趣区域并在多监督下检测疾病。拟议中的新SG-UNet应用多监督缩放功能，无需预先训练即可在全局特征空间中进行优化搜索。此外，变焦损失功能可以逐渐增强对稀疏样本集的聚焦训练。

结果：我们与几种流行的U型整体网络体系结构进行了比较，评估了跨多模态生物医学图像分割任务的U型整体网络体系结构，以从计算机断层扫描（CT），数字结肠镜和组织病理学图像。应用所提出的算法，三个成像模态数据集的Dice系数分别提高了3.116％，2.676％，2.356％，F2-得分分别提高了3.044％，2.420％，1.928％。使用不同数量的直肠癌CT数据的比较结果表明，该算法具有降低边际效率的趋势。腺体分割研究结果还支持与其他最新方法产生可比性能的可行性。

结论：所提出的算法可以通过使用小的图像数据集而无需使用诸如微调之类的其他技术来进行更有效的训练，并且与其他堆叠式集成网络相比，在生物医学图像分割方面具有更高的准确度和更少的计算复杂性。