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.

近年来，弱监督模型在使用乳腺 X 光照片进行肿瘤检测方面发挥了作用，减少了对像素级注释的需求。然而，大部分文献中的现有模型依赖于类别激活图 (CAM) 作为激活方法，忽视了探索其他激活技术的潜在益处。本研究探索并比较了不同的激活图与弱监督训练方法在乳腺 X 光照片中的检测效果。具体而言，我们在 GMIC 模型中研究了 CAM、GradCAM、GradCAM++、XGradCAM 和 LayerCAM 方法，并在 VinDr-Mammo 数据集上进行了评估，使用了精度、真阳性率 (TPR)、假阴性率 (FNR) 和每张图像的假阳性数 (FPPI) 这些度量指标。结果表明，在训练和测试阶段使用不同的激活图策略会改善模型性能。通过这种策略，我们改善了 GMIC 方法的结果，降低了 FPPI 值并增加了 TPR。