通过选择性提取与聚合特征，提升复杂医学图像分类精度。
AGGRNet: Selective Feature Extraction and Aggregation for Enhanced Medical Image Classification
- 区分有用与无用特征，增强细粒度视觉模式识别
- 在多个数据集上达顶尖性能，最高提升5%准确率
- 适合需要高精度诊断的医学图像分析场景
复杂医学图像任务如严重程度分级和疾病亚型分类面临挑战，源于类别间视觉模式相似、标注数据稀缺以及专家判断差异。尽管现有注意力模型能捕捉复杂视觉特征，但其架构常难以区分细微类别，因无法有效建模类间相似性与类内变异性，导致误诊。为此，我们提出AGGRNet框架，通过提取信息性与非信息性特征，更有效地理解细粒度视觉模式，提升复杂医学图像分析的分类性能。实验表明，该模型在多个医学影像数据集上达到当前最优表现，尤其在Kvasir数据集上相较SOTA模型最高提升5%。
原文摘要 · Abstract (English)
Medical image analysis for complex tasks such as severity grading and disease subtype classification poses significant challenges due to intricate and similar visual patterns among classes, scarcity of labeled data, and variability in expert interpretations. Despite the usefulness of existing attention-based models in capturing complex visual patterns for medical image classification, underlying architectures often face challenges in effectively distinguishing subtle classes since they struggle to capture inter-class similarity and intra-class variability, resulting in incorrect diagnosis. To address this, we propose AGGRNet framework to extract informative and non-informative features to effectively understand fine-grained visual patterns and improve classification for complex medical image analysis tasks. Experimental results show that our model achieves state-of-the-art performance on various medical imaging datasets, with the best improvement up to 5% over SOTA models on the Kvasir dataset.
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