In this talk, I will present two machine learning models for the automatic recognition of granulomas within images acquired from in vitro experiments, while addressing the challenge of inter-annotator variability. Such models constitute a valuable tool, as morphological characteristics are believed to be meaningful indicators of the efficacy of drugs targeting Mycobacterium tuberculosis and of the progression of the infection. We address the problem proposing two methods. First, we develop an ensemble model based on three YOLOv11 networks which simulate three different annotators. Second, we employ a hybrid architecture that integrates a YOLOv11 object detection model, serving as a Region Proposal Network (RPN), with a U-Net designed for the high-precision semantic segmentation of the identified Regions of Interest (ROIs). This work has been developed within the ERA4TB project and is based on my Master Thesis, under the supervision of Dr. Enrico Mastrostefano, and in collaboration with Davide Moretti.