The field of medical imaging and diagnosis is moving towards developing more efficient and robust models that can perform well with limited data and computational resources. Researchers are exploring innovative approaches such as regret-minimizing curriculum learning, lightweight architectures, and metric learning to improve the accuracy and reliability of medical diagnosis. These advances have the potential to enable the deployment of advanced computer-aided diagnosis in resource-limited settings, improving patient outcomes and reducing healthcare costs. Noteworthy papers include: FOSSIL, which presents a regret-minimizing weighting framework for difficulty-aware learning in medical imaging, and LightPneumoNet, which introduces a lightweight convolutional neural network for pneumonia diagnosis from chest X-rays. Additionally, the paper on Target-Aware Metric Learning with Prioritized Sampling (TMPS) proposes a simple yet highly adaptable learning framework for robust plant disease diagnosis with few target-domain samples.