Proof of Concept for Mammography Classification with Enhanced Compactness and Separability Modules

TL;DR

This paper validates and extends a recent medical image classification framework, demonstrating its applicability to mammography with enhanced modules, and introduces new evaluation and interpretability tools for clinical use.

Contribution

It adapts and validates an improved ConvNeXt Tiny-based framework for mammography classification, adding multi-metric evaluation, interpretability analysis, and an interactive clinical dashboard.

Findings

GAGM and SEVector improve feature discriminability and reduce false negatives.

Feature Smoothing Loss did not show measurable benefits in mammography.

The framework's modules are effective but may require adaptation for different medical imaging tasks.

Abstract

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.