Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods

TL;DR

This paper investigates various activation map techniques beyond CAM within weakly supervised learning models to enhance mass detection accuracy in mammography images, demonstrating improved performance metrics on a standard dataset.

Contribution

The study systematically compares multiple activation map methods in weakly supervised mammography mass detection, highlighting their impact on model performance.

Findings

Different activation maps improve detection metrics.

Using multiple strategies reduces false positives.

Enhanced TPR and decreased FPPI achieved.

Abstract

In recent years, weakly supervised models have aided in mass detection using mammography images, decreasing the need for pixel-level annotations. However, most existing models in the literature rely on Class Activation Maps (CAM) as the activation method, overlooking the potential benefits of exploring other activation techniques. This work presents a study that explores and compares different activation maps in conjunction with state-of-the-art methods for weakly supervised training in mammography images. Specifically, we investigate CAM, GradCAM, GradCAM++, XGradCAM, and LayerCAM methods within the framework of the GMIC model for mass detection in mammography images. The evaluation is conducted on the VinDr-Mammo dataset, utilizing the metrics Accuracy, True Positive Rate (TPR), False Negative Rate (FNR), and False Positive Per Image (FPPI). Results show that using different strategies of activation maps during training and test stages leads to an improvement of the model. With this strategy, we improve the results of the GMIC method, decreasing the FPPI value and increasing TPR.