The field of deep learning is rapidly advancing in medical imaging and object tracking, with a focus on developing more efficient and accurate models. Researchers are exploring new architectures and techniques, such as hybrid models that combine convolutional neural networks (CNNs) and transformers, to improve performance on complex tasks like retinal vessel segmentation and microtumor detection. Another area of focus is on improving the robustness and generalizability of models, with techniques like hierarchical attention and multi-scale feature fusion showing promise. Notable papers in this area include DL-CapsNet, which proposes a deep and light capsule network for image classification, and HBFormer, which introduces a hybrid-bridge transformer for microtumor and miniature organ segmentation. TinyViT is also noteworthy for its compact pipeline integrating transformer-based segmentation and ensemble regression for solar panel surface fault detection. Overall, these advancements have the potential to improve the accuracy and efficiency of medical imaging and object tracking systems, leading to better clinical outcomes and decision-making.