Traditional deep learning methods are suboptimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal-more likely normal-probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this article proposes a dominant fuzzy fully connected layer (FFCL) for breast imaging reporting and data system (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzifier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean distance to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the curated breast imaging subset of digital database of screening mammography dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.

中文翻译：

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一种基于模糊逻辑的启发式神经网络结构进行图像评分


传统的深度学习方法在对歧义特征进行分类时效果不佳，这些特征通常出现在嘈杂且难以预测的类别中，尤其是在区分语义评分方面。语义评分依靠语义逻辑来实现评价，不可避免地会包含模糊描述，遗漏一些概念，例如正常与可能正常之间的模糊关系总是呈现出不明确的界限（正常-更可能正常-可能正常）。因此，在注释图像时，人为错误很常见。与现有侧重于修改神经网络内核结构的方法不同，本文提出了一种用于乳腺成像报告和数据系统（BI-RADS）评分的主导模糊全连接层（FFCL），并验证了该结构的普适性。该模型旨在开发语义范式的评分互补特性，同时基于分析人类思维模式构建模糊规则，特别是减少语义粘连的影响。具体来说，这个语义敏感的去模糊器层将相关类别所占据的特征投影到语义空间中，并且模糊解码器参考全局趋势修改最后一个输出层的概率。此外，在学习阶段，两个相关类别之间的模糊语义空间会缩小，因为考虑了一个类别在其亲属中出现的正向和负向增长趋势。我们首先使用欧几里德距离来放大真实分数和预测分数之间的距离，然后采用两样本 t 检验方法来证明 FFCL 架构的优势。 对筛查乳腺 X 线摄影数据集的数字数据库的精选乳腺成像子集进行的大量实验结果表明，我们的 FFCL 结构可以在 BI-RADS 评分中的三重分类和多类分类中实现卓越的性能，优于最先进的方法。