Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods
This work addresses mass detection in mammography for medical imaging, but it is incremental as it builds on existing methods by comparing activation techniques.
The study tackled mass detection in mammography images by exploring different activation map methods within a weakly supervised learning framework, resulting in improved performance with decreased false positives per image and increased true positive rates.
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.