The field of microscopy image analysis is moving towards more efficient and accurate methods for image classification and segmentation. Researchers are exploring new approaches to reduce the need for large amounts of labeled data, which is often time-consuming and expensive to acquire. One direction is the use of weakly supervised learning methods, which can leverage uncertainty and attention mechanisms to improve performance. Another area of focus is the development of more robust and interpretable models, such as Neural Cellular Automata (NCA), which can provide scalable and efficient solutions for medical image analysis. Noteworthy papers include: Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells, which proposes a novel approach for weakly supervised segmentation using NCA. Attention Pooling Enhances NCA-based Classification of Microscopy Images, which integrates attention pooling with NCA to enhance feature extraction and improve classification accuracy.