Weakly-supervised tumor segmentation in computed tomography scans

Abstract

This research focuses on the topic of weakly-supervised tumor segmentation. The proposed pipeline involves the usage of a classification model to make predictions regarding the presence of a tumor in an image. Subsequently, the CAM (Class Activation Mapping) approach is employed to identify the most relevant regions within the image as determined by the model. The underlying concept is that the model will learn to identify tumor regions, resulting in higher activations in those areas. The advantage of the weakly supervised approach is its ability to learn from a smaller dataset, requiring only image-level labels in our case. By implementing the proposed pipeline, specifically using the Score-CAM technique.