The field of medical imaging and analysis is rapidly advancing with a focus on improving image quality, disease detection, and patient outcomes. Recent developments have highlighted the importance of data augmentation, multi-scale representation, and prompt tuning in enhancing the accuracy and efficiency of medical imaging tasks. Researchers are exploring novel approaches to address the challenges of limited data, domain gaps, and metal artifacts in medical images. Notably, the integration of multi-view data and advanced deep learning techniques is showing promise in breast cancer detection and neuroscience applications. Furthermore, attention-enabled explainable AI is being applied to predict disease recurrence and provide personalized insights for patient management. Some noteworthy papers in this area include: MediAug, which proposes a unified evaluation framework for advanced data augmentation in medical imaging. Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network, which introduces a novel network for low-dose CT reconstruction and metal artifact reduction. Breast Cancer Detection from Multi-View Screening Mammograms with Visual Prompt Tuning, which presents a novel approach for analyzing multiple screening mammograms. SAM4EM, which leverages the Segment Anything Model for 3D segmentation of complex neural structures in electron microscopy data. Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction, which integrates vector embeddings and attention mechanisms to improve recurrence prediction performance.