Improving Mass Detection in Mammography Images: A Study of Weakly
  Supervised Learning and Class Activation Map Methods
Vicente Sampaio Filipe R. Cordeiro
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.