Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.

中文翻译：

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用于多类眼底病变分割的渐进多尺度一致网络

有效整合多尺度信息对于具有挑战性的眼底病变多类分割具有重要意义，因为不同病变的尺度和形状差异很大。已经提出了几种方法来成功处理多尺度对象分割。然而，在以前的研究中没有考虑到两个问题。首先是相邻特征层级之间缺乏交互，这会导致高层特征与低层特征的偏差，以及细节线索的丢失。二是低层特征和高层特征之间的冲突，这是因为它们学习到不同尺度的特征，从而混淆了模型，降低了最终预测的准确性。在本文中，我们提出了一种渐进式多尺度一致性网络（PMCNet），它集成了提出的渐进式特征融合（PFF）块和动态注意块（DAB）来解决上述问题。具体来说，PFF 块逐步集成来自相邻编码层的多尺度特征，通过聚合细粒度细节和高级语义来促进每一层的特征学习。由于不同尺度的特征应该是一致的，DAB旨在从不同尺度的融合特征中动态学习注意力线索，从而消除多尺度特征中存在的本质冲突。提出的两个 PFF 和 DAB 块可以与现成的骨干网络集成，以解决眼底病变多类分割中的多尺度和特征不一致两个问题，这将在特征空间中产生更好的特征表示。三个公共数据集的实验结果表明，所提出的方法比最近最先进的方法更有效。