乳腺分类的证明概念：增强的紧凑性和可分离性模块
Proof of Concept for Mammography Classification with Enhanced Compactness and Separability Modules
AI总结：
本研究验证了改进的ConvNeXt Tiny架构在乳腺分类中的有效性，通过多指标评估和特征可解释性分析，探讨了增强紧凑性和可分离性的方法。
摘要：
本研究提出了一种近期方法框架在医学图像分类中的验证和扩展。尽管改进的ConvNeXt Tiny架构，集成了全局平均和最大池化融合（GAGM）、轻量级通道注意力（SEVector）和特征平滑损失（FSL），在CPU友好的条件下在阿尔茨海默症MRI上表现出有希望的结果，我们的工作探讨了其在乳腺分类中的可移植性。使用了一个汇总了INbreast、MIAS和DDSM乳腺收集的Kaggle数据集，我们比较了基线CNN、ConvNeXt Tiny和InceptionV3主干，这些主干增强了GAGM和SEVector模块。结果证实了GAGM和SEVector在提高特征可区分性和减少假阴性方面的有效性，特别是对于恶性病例。然而，在我们的实验中，特征平滑损失在乳腺分类条件下没有产生可测量的改进，这表明其有效性可能取决于特定的架构和计算假设。除了验证外，我们的贡献通过多指标评估（宏F1、每类召回方差、ROC/AUC）、特征可解释性分析（Grad CAM）以及为临床探索开发的交互式仪表板扩展了原始框架。作为展望，我们强调需要探索替代方法以提高类内紧凑性和类间可分离性，具体目标是增强乳腺分类中恶性与良性病例的区分。
英文摘要：
This study presents a validation and extension of a recent methodological framework for medical image classification. While an improved ConvNeXt Tiny architecture, integrating Global Average and Max Pooling fusion (GAGM), lightweight channel attention (SEVector), and Feature Smoothing Loss (FSL), demonstrated promising results on Alzheimer MRI under CPU friendly conditions, our work investigates its transposability to mammography classification. Using a Kaggle dataset that consolidates INbreast, MIAS, and DDSM mammography collections, we compare a baseline CNN, ConvNeXt Tiny, and InceptionV3 backbones enriched with GAGM and SEVector modules. Results confirm the effectiveness of GAGM and SEVector in enhancing feature discriminability and reducing false negatives, particularly for malignant cases. In our experiments, however, the Feature Smoothing Loss did not yield measurable improvements under mammography classification conditions, suggesting that its effectiveness may depend on specific architectural and computational assumptions. Beyond validation, our contribution extends the original framework through multi metric evaluation (macro F1, per class recall variance, ROC/AUC), feature interpretability analysis (Grad CAM), and the development of an interactive dashboard for clinical exploration. As a perspective, we highlight the need to explore alternative approaches to improve intra class compactness and inter class separability, with the specific goal of enhancing the distinction between malignant and benign cases in mammography classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
